, Department of Electrical Engineering, Princeton University

, Department of Electrical Engineering and Computer Sciences, University of California

, SRI International

, Department of Electrical Engineering and Computer Sciences, University of California

, SRI International

, SRI International

, Department of Electrical Engineering and Computer Sciences, University of California

, Department of Electrical Engineering, Princeton University

Pages: 63–80

Abstract—Integrated circuits (ICs) are now designed and fabricated in a globalized multivendor environment making them vulnerable to malicious design changes, the insertion of hardware Trojans/malware, and intellectual property (IP) theft. Algorithmic reverse engineering of digital circuits can mitigate these concerns by enabling analysts to detect malicious hardware, verify the integrity of ICs, and detect IP violations. In this paper, we present a set of algorithms for the reverse engineering of digital circuits starting from an unstructured netlist and resulting in a high-level netlist with components such as register files, counters, adders, and subtractors. Our techniques require no manual intervention and experiments show that they determine the functionality of Z_${>}{45\%}$ _Z and up to 93% of the gates in each of the test circuits that we examine. We also demonstrate that our algorithms are scalable to real designs by experimenting with a very large, highly-optimized system-on-chip (SOC) design with over 375000 combinational elements. Our inference algorithms cover 68% of the gates in this SOC. We also demonstrate that our algorithms are effective in aiding a human analyst to detect hardware Trojans in an unstructured netlist.

Keywords—Digital circuits; computer security; design automation; formal verification

Contemporary integrated circuits (ICs) are designed and fabricated in a globalized, multi-vendor environment due to which ICs are vulnerable to malicious design changes and the insertion of hardware trojans and malware. The possibility that malicious chips might be used in sensitive locations such as military, financial and government infrastructure is a serious and pressing concern to both the users and designers of contemporary ICs ^{[1]}, ^{[8]}, ^{[15]}, ^{[17]}, ^{[28]}. For example, the DARPA IRIS program seeks to develop techniques for reverse engineering digital, analog and mixed-signal ICs to determine their integrity for use in sensitive installations ^{[5]}. Algorithmic approaches to reverse engineering chips can aid in the detection of hardware trojans, malicious design changes and in verifying the integrity of untrusted design components for which trustworthy source code may not be available. Reverse engineering is also important in detecting intellectual property violations, considered a “serious concern” for the semiconductor industry ^{[7]}.

In this paper, we study a portfolio of fully algorithmic approaches to reverse engineer digital circuits. We analyze an unstructured netlist with the objective of inferring a high-level netlist with components such as register files, adders and counters. The key challenge in analyzing an unstructured netlist is that *we have no information about the boundaries of the modules * contained in the netlist. Therefore, we tackle the reverse engineering problem through a variety of algorithms that “carve out” portions of the netlist to *generate potential/candidate modules * and employ techniques similar to those used in design synthesis and verification to determine the functionality of these modules. In particular, this paper focuses on algorithmic reverse engineering of *datapath * components in an unstructured netlist. The objective is to aid a human analyst understand the functionality of an unstructured netlist by algorithmically identifying as many components as possible.^{1}

Fully algorithmic reverse engineering is a relatively new field of research. Previous work primarily suggests strategies of attack for a human analyst ^{[9]}, ^{[29]}. For example, in their investigation of the ISCAS '85 benchmarks, Hansen *et al. * analyze replicated structurally isomorphic blocks ^{[9]}. The cut-based Boolean matching and aggregation algorithms presented in Sections II-A and II-B are generalizations of this idea.

A recent attempt at addressing the reverse engineering problem algorithmically is by Li *et al. *^{[14]}. They present a method for behavioral matching of an unknown sub-circuit against a library of *abstract components * but assume that methods are available to generate sub-circuits from the unstructured netlist. Therefore, our set of solutions is complementary to theirs because: (a) we target different kinds of components for reverse engineering and (b) we analyze an unstructured netlist as opposed to sub-circuit matching.

An alternative approach to malware detection relies on comparing side channel signals such as power and timing between the trusted design and untrusted versions of the designs. For instance, Agrawal *et al. * compare “fingerprints” consisting of measurements of power, electromagnetic and thermal emissions ^{[2]}. Wang *et al. * use differences in current measurements to detect trojans ^{[31]}. Jin *et al. * compare path delay measurements ^{[11]}. These approaches assume that a trusted (“known good”) version of the chip is available for experimentation, something that may not be true when untrusted component IPs are used, the foundry itself is untrusted or when it is not possible to determine trustworthy chips by destructive examination.

Architectural approaches to trojan detection and avoidance have also been proposed. Hicks *et al. *^{[10]} proposed an analysis that detects pairs of circuit nodes that are not exercised by design verification tests. They suggested that these nodes could potentially be used to hide trojans and proposed an architectural technique that eliminates such nodes from the circuit and emulates their designed functionality through software.^{2} Waksman *et al. *^{[30]} proposed a set of transformations that permute module inputs, the order in which inputs are applied, and obfuscate reset sequences in order to prevent Trojan activation. Both proposals assume availability of RTL source and design verification tests for the design being analysed.

Trojan detection through algorithmic reverse engineering does not rely on any of these assumptions. Hence, it can detect a wider range of malware, including, for example, bugs/malware introduced by design automation tools. This additional coverage comes at a cost, which is that traditionally reverse engineering has been a labor-intensive process. We show that fully algorithmic reverse engineering is both feasible and effective even for very large designs. A comparison of the differences in assumptions of availability and threat models for the techniques discussed above is shown in Table I.

TABLE I Comparing Techniques for Trojan Detection With This Work

This paper builds upon our past work in the area of algorithmic reverse engineering in ^{[13]} and ^{[26]}. A detailed discussion of the differences between these past efforts and this paper is deferred to Section VI-B-V. The problem of deriving a gate-level netlist from a physical chip is outside the scope of this work. This has been studied in ^{[12]}, ^{[19]}, ^{[20]}, ^{[27]}, and ^{[29]}. Nohl *et al. * derive a gate-level netlist of an RFID tag and examine it for cryptographic vulnerabilities ^{[20]}. Kömmerling *et al. * describe techniques for obtaining gate-level descriptions of smartcard processors ^{[12]}. Tarnovsky used electron microscopy and bus-level probing to reverse engineer an Secure Infineon Processor which included mesh shielding inserted in order to thwart reverse engineering ^{[27]}.

In this subsection, we describe the assumptions and objectives underlying this work and then provide an overview of our solution.

We tackle the problem of reverse engineering a gate-level netlist under the following assumptions. First, we assume that register transfer language (RTL) source code for the test article being analyzed is Z_$not$ _Z available. We also assume that micro-architectural information as well as design-specific information pertaining to the test article being analyzed is also Z_$not$ _Z available. We only assume availability of “datasheet-level” information which usually consists of a high-level description of the functionality of the test article and a description of its input/output pin interface.

Given these assumptions our target is to algorithmically derive information about high-level components present in the test article by analyzing the gate-level netlist.

In both the (a) trojan-detection and (b) intellectual property infringement usage scenarios, our assumptions correspond to an external analyst examining a test article to determine if it (a) contains hardware trojans or (b) infringes relevant intellectual property. Note that the analyst does not have access to source code for the design. Therefore, traditional techniques for trojan detection ^{[2]}, ^{[11]}, ^{[31]} are not applicable. Furthermore, the only plausible alternative to algorithmically assisted reverse engineering is full manual inspection of the netlist.

In case the assumptions for trojan detection scenario are weaker than ours, we note that our techniques are complementary to the other trojan detection techniques discussed in the previous section. For example, when considering the threat of trojans inserted in the RTL or by design automation tools, analysis techniques like UCI ^{[10]} can be synergistically applied along with algorithmic reverse engineering of the gate-level synthesized netlist and correlating identified components with those expected to be present. Note UCI itself cannot detect trojans inserted by design automation tools. Furthermore, all techniques studied in this paper rely on a “static” analysis of the netlist and do not consider information derived from simulations. Combining these algorithms with simulation-based “dynamic” analysis techniques will likely yield interesting results because these approaches are complementary.

Our algorithms focus on identifying datapath components. The are three reasons for this design choice. First, any attack model that is based on triggering malicious behavior through a rare input sequence will necessarily involve some manipulation of the datapath. In fact, as illustrated inSection V-D, it is likely that this malicious logic will manifest as a collection of datapath components such as counters, decoders and multiplexers. Thus identifying such components will help an analyst quickly zero-in on problematic parts of the netlist. Second, datapath components exhibit regularity and structure and are amenable to algorithmic analysis. Finally, the majority of the gates in processor-like circuits are in the datapath. The focus on datapath components means that it will not be possible to reverse-engineer 100% of the gates in the design. However, as will be shown in Section V-D, this is not a major limitation for detecting hardware trojans.

It is important to note that this work develops a set of algorithms that Z_$aid$ _Z trojan detection. By providing a human analyst with an abstracted netlist containing high-level components, it makes the job of the analyst *much easier * than if the analyst were to individually examine hundreds and thousands of gates and latches. The tool itself *does not * perform trojan detection.

The objective of our work is to infer a useful high-level description from an unstructured gate-level netlist. In particular, we focus on reverse engineering *datapath elements * in digital circuits. Even when focusing primarily on the datapath, reverse engineering is still a very hard problem because we are starting with a sea of gates for the complete chip, including the datapath as well as the control logic, and it is not obvious how to go about finding some meaningful subset of the gates/latches for algorithmic analysis. Hence, our approach integrates a number of different techniques tackling different aspects of the problem. Fig. 1 shows the techniques we introduce and their inter-relationships.

Fig. 1. Portfolio of the reverse engineering techniques introduced in this paper. Superscripts refer to items in our list of reverse engineering algorithms introduced by this work. Algorithms 1–5 identify combinational modules while algorithms 6–9 identify sequential modules.

Our strategy is to attack the problem in two stages. The first stage identifies *potential module boundaries * using topological/functional analyses. The second stage *functionally analyzes potential modules * to understand their behavior.

The reverse engineering algorithms introduced by this paper are as follows.

- We present a novel application of cut-based Boolean matching to find replicated bitslices
^{3}in the netlist. This analysis helps us find circuit nodes that correspond to functions such as 1-bit adders and 1-bit multiplexers. - We present algorithms that topologically analyze the results of bitslice matching to
*aggregate*multibit components such as multiplexers, adders and subtractors. - Analyzing aggregated modules helps identify bits which are operated upon simultaneously, allowing us to infer
*words*. These inferred words are then used in our word propagation algorithm to generate additional words. - Our module generation algorithm analyzes words which are structurally connected to generate
*candidate unknown modules*. These are potential operators with word arguments and results and are matched against a component library using a Quantified Boolean Formula (QBF) formulation. - We present an alternate strategy to infer combinational modules like decoders using a BDD-based analysis of nodes with common inputs.
- We present novel algorithms that identify word-level registers, as well register array structures like register files and RAM arrays using a BDD-based functional analysis.
- We present algorithms to identify counters and shift registers using topological analyses combined with a satisfiability (SAT) checking formulation.
- Modules inferred by the above-mentioned portfolio of algorithms may “overlap”,
*i.e.*, cover the same elements.^{4}These overlaps are resolved by formulating an integer linear program (ILP) that selects a non-overlapping subset of inferred modules that optimizes a set of desired metrics.

The contributions of this paper include the reverse engineering algorithms listed above as well as the analysis flow shown in Fig. 1. We also present a detailed evaluation of our algorithms by experimenting with eight unstructured netlists, details of which are shown in Table II. The netlists marked with a dagger Z_$(\dagger)$ _Z were obtained by synthesizing designs from opencores.org. Results show that our inference algorithms determine the functionality of more than 45% and up to 93% of gates in the designs in a fully automated manner.

TABLE II Netlists Used in Experiments

Furthermore, we present a case study of algorithmic reverse engineering of a large highly-optimized system-on-chip (SoC) design consisting of over 375,000 combinational elements. We show a large design like this can be analyzed through logic simplification and module partitioning. Our results show that 68% of the gates left after simplification in this SoC were covered by our inference algorithms. We believe our work is the first effort to algorithmically reverse engineer the majority of the gates in a design of this scale.

A final important contribution of our work is a case study of how our algorithms could aid the detection of hardware trojans. We inject trojans into two test articles and discuss how algorithmic reverse engineering helps a human analyst detect this malicious circuitry.

The rest of this paper is organized as follows. Section II describes our algorithms for identifying combinational components. Section III describes our algorithms for identifying sequential components. Section IV describes how possibly conflicting inference results from different algorithms can be resolved to produce the final set of inferred modules. Section V presents the experimental evaluation of these algorithms. Section VI discusses limitations and avenues for further analysis. Section VII provides some concluding remarks.

The section describes algorithms to identify fully combinational modules. Our first algorithm is based on the observation that many datapath elements consist of replicated bitslices connected in a specific topology and is described in Sections II-A and II-B. We then present algorithms for identifying word-level modules in Sections II-C and II-D. Finally, Section II-E presents a third attack on combinational modules using a specific topological property.

The goal of bitslice identification is to identify all nodes in the circuit that match functions from a bitslice library. For instance, we might be interested in finding all nodes that match the full adder carry function Z_$f(a,b,c)=ab+bc+ca$ _Z, this might help identify multibit adders. We adopt a *functional * matching approach, which matches based on the function implemented by a set of gates instead of matching structural patterns. This uses cut-enumeration and Boolean matching, which was initially introduced for technology mapping ^{[3]}, ^{[4]}.

A *feasible cut * of a circuit node Z_$G$ _Z is defined as a set of nodes in the transitive fan-in cone of Z_$G$ _Z such that a consistent assignment of truth values to each node in the set completely determines the value of Z_$G$ _Z^{[3]}. A cut is said to be *k-feasible * if it has no more than Z_$k$ _Z inputs. The trivial cut Z_$\{G\}$ _Z is always *k-feasible *. The set of *k-feasible * cuts for a gate is recursively computed by enumerating the union of all *k-feasible * cuts of the gate's inputs such that this union has Z_$k$ _Z or fewer inputs.

Our tool enumerates all 6-feasible cuts. We found that the average number of 6-feasible cuts per gate is between 15 and 35. The number of cuts for Z_$k>6$ _Z is significantly higher. ^{5} Although we are restricted to bitslices with six or fewer inputs, this is not a major limitation as most common bitslices have less than six inputs; e.g., a full adder bitslice has 3 inputs.

Once all cuts are identified, they are grouped into equivalence classes using permutation-independent Boolean matching. For example, nodes matching the function Z_$f(a,b,c)=ab+c$ _Z and nodes matching Z_$f(a,b,c)=bc+a$ _Z are grouped into the same class. Each equivalence class may match a known library function.

Now that we have all the nodes that match a particular function, the next step is to look for matching nodes connected in interesting patterns. *Aggregating * replicated bitslices which are connected in specific patterns is our first technique for identifying combinational modules. The following subsections expand on our aggregation algorithms.

This algorithm considers all bitslices that match a particular function and groups them using common input signals. For instance, consider the function that represents a 2:1 multiplexer: Z_$f(a,b,s)=sa+\lnot{s}b$ _Z. Here we group all matching bitslices which have a common^{6} select signal (Z_$s$ _Z in this example). Common signal aggregation finds 59 decoders and 140 multiplexers in the *RISC FPU * test article.

Besides aggregating functions in the bitslice library such as multiplexers and decoders, we can also aggregate unknown functions connected by a common signal to generate candidate unknown modules. These modules may be analyzed either by a human analyst or by a permutation and phase independent matching algorithm such as ^{[18]}.

In this case, the algorithm considers all bitslices matching a particular function such that the output of one bitslice is the input of another (e.g., carry chain in a ripple carry, parity tree). Propagated signal aggregation finds 37 adders/subtractors and 10 parity trees in the *RISC FPU * test article.

Aggregated bitslices tell us about circuit nodes that are operated upon simultaneously. These nodes are likely to form part of same *word *. Our tool groups the bits that are inputs/outputs of aggregated modules into “word” data structures.

Once some words are identified, more words can be generated by *propagating * them across gates. The idea is, given a word Z_$w$ _Z, find conditions under which its value is propagated to a new word Z_$w^{\prime}$ _Z, for every possible value of Z_$w$ _Z.

Consider the circuit in Fig. 2. Note that the circuit behaves as a selector between the bitwise negation of the word Z_$u_{1}$ _Z, Z_$u_{2}$ _Z, Z_$u_{3}$ _Z and the bitwise negation of the word Z_${{v}}_{1}$ _Z, Z_${{v}}_{2}$ _Z, Z_${{v}}_{3}$ _Z depending on the value of Z_$c$ _Z. Hence, the negated value of Z_$u_{1}$ _Z, Z_$u_{2}$ _Z, Z_$u_{3}$ _Z gets propagated to Z_${{w}}_{1}$ _Z, Z_${{w}}_{2}$ _Z, Z_${{w}}_{2}$ _Z if Z_$c=0$ _Z, and the negated value of Z_$u_{1}$ _Z, Z_$u_{2}$ _Z, Z_$u_{3}$ _Z gets propagated to Z_${{w}}_{1}$ _Z, Z_${{w}}_{2}$ _Z, Z_${{w}}_{2}$ _Z, otherwise. These are the kind of claims produced by the word propagation algorithm.

Fig. 2. Example: word propagation.

For efficiency reasons, we use symbolic simulation, which allows consideration of all possible values of Z_$w$ _Z simultaneously in a single run. Similar to Roth's D-calculus ^{[22]}, we redefine functions of logic gates in the circuit operating on the expanded domain Z_$\{0, 1, D,\bar{D}, X\}$ _Z, where Z_$D$ _Z represents a symbolic value in Z_$\{0, 1\}$ _Z, Z_$\bar{D}$ _Z is the negation of Z_$D$ _Z, and Z_$X$ _Z represents an unknown value. Some examples of symbolic evaluation are: Z_$and(D, 1)=D$ _Z, Z_$and(D, 0)=0$ _Z, Z_$and(0, X)=0$ _Z, Z_$not(X)=X$ _Z, and Z_$not(D)=\bar{D}$ _Z.

Our word propagation algorithm follows a “guess and check” approach. Given an initial word Z_$w$ _Z, the guessing stage consists on finding a set Z_$S$ _Z of potential “target words” for the propagation. Such set Z_$S$ _Z is computed by grouping the outputs of the gates driven by the signals in Z_$w$ _Z by gate type and port they connect to. Then, for each target word Z_$w^{\prime}\in S$ _Z, a set Z_$C$ _Z of *control wires * is computed as the set of wires lying in the intersection of the fanins (up to a small depth Z_$k$ _Z) of the gates whose output is in Z_$w^{\prime}$ _Z. The checking stage consists on running several symbolic simulations of the local netlist that is relevant to the propagation that is being checked. In such simulations, the inputs of such local netlist are initialized as follows.

- Each bit of Z_$w$ _Z is set to the symbolic value Z_$D$ _Z.
- For each combination of 3 wires taken from the set of all control wires, all possible binary values are evaluated.
- The rest of the inputs of the local netlist are assigned Z_$X$ _Z.

A simulation with a particular partial assignment Z_$\sigma$ _Z to the control wires succeeds if all wires in the target word evaluate to either Z_$D$ _Z or Z_$\bar{D}$ _Z. In that case, Z_$w$ _Z propagates to Z_$w^{\prime}$ _Z under Z_$\sigma$ _Z and Z_$w^{\prime}$ _Z can be tested for further propagation.

An analogous approach in which Z_$w^{\prime}$ _Z is guessed among the structural predecessors of Z_$w$ _Z and it is checked whether Z_$w^{\prime}$ _Z can be propagated to Z_$w$ _Z allows to test for *backward propagation *.

The two main limitations of bitslice identification are: (i) we are limited to bitslices with a maximum of 6 inputs due to the Z_$k\leq 6$ _Z limitation on cut-enumeration and (ii) it is difficult to identify combinational structures that do not have a clean interconnection pattern. Our second approach overcomes these limitations by constructing entire modules and then matching them against a component library.

The intuition here is that since datapath circuits operate on word inputs and produce word outputs, cutting out portions of the circuit that exist *between words * may find interesting candidate modules. Our module identification algorithm identifies combinatorial *candidate unknown modules * operating on words and checks equivalence against a set of predefined reference modules implementing common operations such as addition, subtraction, boolean operations, and shifting/rotation.

For example, consider words Z_${{w}}_{1}$ _Z, Z_${{w}}_{2}$ _Z, Z_$w$ _Z, and the largest combinatorial sub-circuit Z_$C$ _Z having Z_${{w}}_{1}$ _Z and Z_${{w}}_{2}$ _Z as inputs and Z_$w$ _Z as output. Additionally, Z_$C$ _Z may have additional inputs, to which we refer as *side inputs *. Let Z_$Y$ _Z be the set of all side inputs of Z_$C$ _Z. Due to optimizations introduced during the synthesis process or simply a design decision, the function implemented by Z_$C$ _Z might not be unique since the values of some of the wires in Z_$Y$ _Z determine the operation implemented by Z_$C$ _Z, e.g, addition or subtraction. For this reason, given a reference module Z_$C^{\prime}$ _Z we model our equivalence checking as a 2QBF ^{7} satisfiability question: is there any value for the wires in Z_$Y$ _Z such that, for every value of the inputs Z_${{w}}_{1}$ _Z, Z_${{w}}_{2}$ _Z, Z_$C$ _Z and Z_$C^{\prime}$ _Z give the same output? More concretely, we construct a miter formula Z_$\Phi (X,Y)$ _Z from Z_$C$ _Z and Z_$C^{\prime}$ _Z by inserting a comparator between their respective outputs. Then, using a state-of-the-art QBF solver, we find values for the side inputs in Z_$Y$ _Z for Z_$C$ _Z to match the function implemented by Z_$C^{\prime}$ _Z. This is illustrated in Fig. 3.

Fig. 3. QBF formulation showing Miter construction.

The module matching algorithm was able to identify the 8-bit ALU performing addition, subtraction, rotation and negation in the *oc8051 * test article. Each operation is performed for a different setting of the side inputs, so this module cannot be detected through bitslice aggregation. The ability to create and identify word-level modules was key here.

In this section we introduce an algorithm that detects modules that do not necessarily have word inputs or outputs or consist of small replicated bitslices. This analysis technique can be used to detect combinational modules with the specific property that each of the outputs of the module depend on the same set of inputs.

Examples of modules which satisfy this property are decoders, demultiplexers and population counters. Note that modules like adders and multipliers *do not * satisfy this property. Output bit 0 of an adder only depends on the two least significant bits of the addend and the augend, while output bit Z_$k$ _Z of the adder depends on the Z_$k$ _Z least significant bits of the addend and the augend respectively.

Consider the full combinational fanin cone of a combinational node in the circuit. The inputs of this cone are the chip inputs and latch outputs. Suppose two combinational nodes in the circuit are computed using the same set of circuit nodes, it is clear that the inputs of the full combinational fanin cone of these nodes will also be the same.

Therefore, we can group nodes into equivalence classes in the following way. Two nodes are placed in the same class iff the inputs of their full combinational fanin cones are the same. These equivalence classes can computed efficiently using a union-find data structure and give us candidate output nodes with the property that they are fully determined by the same set of inputs.

Consider the example shown in Fig. 4. Nodes Z_$z_{1}$ _Z and Z_$z_{2}$ _Z will be grouped in the same equivalence class because they are completely determined by the same set of chip inputs and latches: Z_$x_{1}~\ldots~x_{9}$ _Z. Such nodes (Z_$z_{1}$ _Z and Z_$z_{2}$ _Z) will form the outputs of the candidate module.

Fig. 4. Nodes with common support.

However, to determine the module boundary we still need to find the inputs of the module, *i.e., * nodes Z_$y_{1}$ _Z and Z_$y_{2}$ _Z in Fig. 4. The module boundary is given by the set of nodes in the full combinational fanin cone of the candidate outputs which are *not present * in the intersection of each of these fanin cones. It is visually clear from the figure that intersection of the combinational fanin cones contains only module 1, so the nodes in the fanin cone which are not present in the intersection leaves us with correct module boundary.

We use a BDD-based formulation to verify the properties of the modules generated by the algorithm in Section II-E-I.

To verify whether a potential module is a decoder or demultiplexer, all that needs to be done is to prove that each output is satisfiable and that no two outputs of the module are simultaneously high.^{8} This can be verified in a straightforward manner using a BDD-based analysis.

A population counter can be detected using a similar algorithm which uses BDD-based matching to compare the function of each output node against the function representing each output bit of a population counter.^{9}

Modules generated by the inference algorithms described in this section are subject to a post-processing step that “fuses” certain types of modules to generate larger modules. This increases the level of abstraction of the inferred modules and makes the inference output easier to understand. For example, 2:1 muxes, 3:1 muxes and 4:1 muxes which are adjacent to each other are fused to form larger Z_$n{:}1$ _Z muxes. Similarly, decoders whose outputs drive the select inputs of muxes are fused with the muxes to form routing structures. Module types which can be fused in this manner are said to be compatible.

Module fusion is performed by first constructing a module fusion graph. The nodes in the graph are modules and an edge between Module A and Module B exists in the graph if and only if all the outputs of module A are inputs of module B and respective module type are compatible. Once the module fusion graph is constructed, connected components in the graph are fused to form a larger combinational module and the resulting module is *added * to collection of inferred modules. The constituent modules which were the “inputs” of the fusion are Z_$not$ _Z eliminated at this stage. The overlap elimination algorithm (see Section IV) determines which of these modules (fused vs. constituents) is included in the output.

A reverse engineering solution must identify commonly occurring sequential components such as RAM arrays, register files, counters and shift registers because these cover a significant number of gates in circuits and also give insight into functionality of the circuit. The challenge here is again in finding meaningful module boundaries for these components given the unstructured netlist. Our strategy is to devise topological analyses to find circuit nodes that are *potential * counters, RAM outputs or shift registers. We then formulate functional analyses using SAT and BDDs that verify correctness of the “guess” made by the topological analysis. The rest of this section presents algorithms to identify RAM arrays/register files, counters, shift registers and multibit registers.

The specific problem in counter identification is to identify sets of latches in the unstructured netlist that behave like counters. The difficulty here is twofold. First, given a set of latches that we suspect to implement a counter, we need a functional analysis that can verify its properties. Second, we need an efficient algorithm to enumerate candidate counters. Simply considering all subsets of latches is computationally infeasible.

Based on this observation, our analysis is performed in two stages. First, potential counters are generated by finding sets of latches whose interconnections match the counter topology shown in Fig. 5. The intuition for this topology is that bit Z_$i$ _Z of a Z_$n$ _Z-bit up counter toggles when the lower order bits Z_$1\ldots i-1$ _Z are all high. Therefore, there needs to be a combinational path from the outputs of these latches to the input of bit Z_$i$ _Z, leading to the topology shown in the figure.

Fig. 5. Latch-to-latch information flow in a counter: each latch in the counter is driven by the latches corresponding to the lower-order bits.

The next step uses a SAT-based functional analysis to verify whether the functions at the inputs of the latches in the counter satisfy the following conditions: (i) each latch toggles either when all the low-order latches are 1 (up counter) or all the low-order latches are 0 (down counter) and (ii) the conditions that control when the counter is enabled/reset are the same for all the bits of the counter.

The latch connection graph (LCG) is an unweighted graph Z_$G=(V,E)$ _Z which formalizes the notion of information flow between latches. The vertices of the graph Z_$(V)$ _Z are the latches and flip-flops in the netlist being analyzed. A directed edge Z_$({{v}}_{1},{{v}}_{2})\in E$ _Z iff there is a combinational path from the output of latch Z_${{v}}_{1}$ _Z (its Q node) to the input of latch Z_${{v}}_{2}$ _Z (its D node).

Given the LCG, we find subgraphs which have the topology shown in Fig. 5. More precisely, given the LCG Z_$G=(V, E)$ _Z, we find ordered sets of nodes Z_$V_{c}=\{{{v}}_{1},\ldots,{{v}}_{k}\}$ _Z, such that Z_$V_{c}\subset V$ _Z and Z_$\forall {{v}}_{i},{{v}}_{j}\in V_{c}: ({{v}}_{i},{{v}}_{j})\in E$ _Z iff Z_$i\leq j$ _Z.

We now devise a functional analysis that verifies that the “candidate” counter found by the topological analysis has the properties of a counter. First, let us formalize the behavior of an up counter as follows.^{10}$$\eqalignno{c_{i}=&\,\lnot r\land e\land (q_{1}\land q_{2}\land\cdots\land q_{i-1})\land\lnot{q_{i}}\lor\cr &\lnot r\land e\land (\lnot{q_{1}}\lor\lnot{q_{2}}\lor\cdots\lor\lnot{q}_{i-1})\land q_{i}\lor\cr &\lnot r\land\lnot{e}\land q_{i}\lor s&{\hbox{(1)}}}$$

In the equation above, Z_$c_{i}$ _Z determines the next state of bit Z_$i$ _Z of an Z_$n$ _Z-bit counter. Z_$r$ _Z is the function that resets the counter, Z_$s$ _Z is the function that sets its value high, Z_$e$ _Z is the count-enable function, and Z_$q_{1}\ldots q_{i}$ _Z are the current values of latches Z_$1\ldots i$ _Z of the counter.

Equation (1) says that bit Z_$i$ _Z toggles when all the lower order bits Z_$(1\ldots i-1)$ _Z are high, the counter is enabled and not being reset. The bit retains its value when the counter is enabled, not reset but one of the lower order bits is zero. The counter also holds its value when it is not reset and not enabled. Bit Z_$i$ _Z is pulled high if the set function evaluates to 1. Note since we have left the functions Z_$r$ _Z, Z_$e$ _Z and Z_$s$ _Z unspecified, Z_$c_{i}$ _Z is actually *a family of functions * and not a specific Boolean function.

Now consider the Boolean function defined by the full combinational fanin cone for each latch in the candidate counter. Let this function be denoted by Z_$d_{i}$ _Z where Z_$i$ _Z ranges across the bits of the counter. We compute the following cofactors from Z_$d_{i}$ _Z.^{11}$$\eqalignno{f_{i}=&\,{cofactor}(d_{i}, q_{1}\land q_{1}\land\cdots\land q_{i-1}\land\lnot q_{i})\cr g_{i}=&\,{cofactor}(d_{i}, q_{1}\land q_{1}\land\cdots\land q_{i-1}\land q_{i})\cr h_{i}=&\,{cofactor}(d_{i},(\lnot{q_{1}}\lor\cdots\lor\lnot{q}_{i-1})\land q_{i})}$$

The insight here is that if the function Z_$d_{i}$ _Z is compliant with Z_$c_{i}$ _Z from Equation (1), then the functions Z_$f_{i}$ _Z, Z_$g_{i}$ _Z and Z_$h_{i}$ _Z will reduce to Equation (2). $$\eqalignno{f_{i}=&\, (\lnot r\land e)\lor s\cr g_{i}=&\,(\lnot r\land\lnot e)\lor s\cr h_{i}=&\,\lnot r\lor s&{\hbox{(2)}}}$$

Now, the functions Z_$r$ _Z, Z_$s$ _Z and Z_$e$ _Z should be the same for all the bits in the counter. Hence, Z_$f_{i}$ _Z, Z_$g_{i}$ _Z and Z_$h_{i}$ _Z must also be equivalent. Therefore, we can determine that a set of latches is not a counter if the SAT solver finds that the functions Z_$f_{i}$ _Z, Z_$g_{i}$ _Z and Z_$h_{i}$ _Z are not equivalent for all Z_$i$ _Z.

Five counters were found in the *oc8051 * test article.

As with counters, our goal here is to identify sets of latches that form shift registers given an unstructured netlist. The shift register identification algorithm is similar to the counter identification algorithm in that it uses a topological check and a SAT formulation except that the topology and verification conditions differ.

The topological check for shift registers uses a pruned version of the latch connection graph (LCG) that we call the single path latch connection graph (SPLCG). As in the LCG, the nodes in the SPLCG are the latches and flip-flops in the netlist. However, the edge Z_${{v}}_{1}\to{{v}}_{2}$ _Z exists in the SPLCG iff there is exactly one combinational path from the output of latch Z_${{v}}_{1}$ _Z to the input of latch Z_${{v}}_{2}$ _Z.

The topological check for unidirectional shift registers is as follows. Given the SPLCG Z_$G=(V,E)$ _Z we find ordered sets of nodes Z_$V_{s}=\{{{v}}_{1},{{v}}_{2},\ldots,{{v}}_{k}\}$ _Z such that Z_$V_{s}\subset V$ _Z and Z_$\forall {{v}}_{i},{{v}}_{j}\in V_{s}: ({{v}}_{i},{{v}}_{j})\in E$ _Z iff Z_$j=i+1$ _Z. In other words, we are searching for chains of latches connected by exactly one combinational path between each latch and its successor.

We model the family of functions representing the next-state function of bit Z_$i$ _Z of a shift register using the following equation. $$s_{i}=\lnot r\land (e\land q_{i-1}\lor\lnot e\land q_{i})\lor s\eqno{\hbox{(3)}}$$

Z_$r$ _Z, Z_$s$ _Z and Z_$e$ _Z are the reset, set and enable functions respectively. Z_$q_{i}$ _Z is the output of the Z_$i$ _Zth latch of the shift register. Suppose Z_$d_{i}$ _Z is the Boolean function determined by the full combinational fan-in cone of latch Z_$i$ _Z of the supposed shift register. As in the counter analysis, we consider the following cofactors of Z_$d_{i}$ _Z.$$\eqalignno{f_{i}=&\,{cofactor}(d_{i}, q_{i-1}\land\lnot q_{i})\cr g_{i}=&\,{cofactor}(d_{i},\lnot q_{i-1}\land q_{i})}$$

If Z_$d_{i}$ _Z is complaint with Z_$s_{i}$ _Z we will have: $$\eqalignno{f_{i}=&\,\lnot r\land e\lor s\cr g_{i}=&\,\lnot r\land\lnot e\lor s}$$

Therefore, the functional check verifies that the functions Z_$f_{i}$ _Z and Z_$g_{i}$ _Z are identical for each bit of the shift register.

Shift registers may consist of multiple bits shifting in tandem from one set of latches to another. The basic algorithm finds each cascading chain of latches as separate shift registers. To *aggregate * shift registers, first we group shift registers by length. Next, we form equivalence classes within each group where shift registers with the same set, reset and shift-enable functions are classified together. Finally, each equivalence class is output as a multibit shift register module.

Seven shift registers were found in the *RISC FPU * test article.

This section targets small RAM arrays and register files. Our objective here is to find the latches/flip-flops that form the RAM, associated logic that reads data (called “read-logic”) and logic that writes data into the latches (called “write-logic”).

The intuition behind identifying the read-logic is that it forms a set of trees with the RAM cells as leaves of the tree and the read outputs as roots. We use a marking algorithm to find such trees. Initially, the algorithm marks all latches in the netlist. Subsequently, it marks all gates which satisfy the following conditions: (i) at least one of the gate's inputs is marked and (ii) the gate has only one fanout. This is repeated until no new nodes are marked.

The next step is a functional analysis of the marked nodes. A BDD is constructed for each marked node in terms of the latches, inputs and unmarked nodes in the circuit. Among the inputs of this BDD, we assume that those which are latches are storage nodes Z_$(l_{i})$ _Z while the remaining are the read address Z_$(s_{i})$ _Z. We then verify the following properties. Latches and nodes which pass these checks are identified as the RAM array and its corresponding read-logic.^{12}

- If Z_$y=f(s_{1},\ldots, s_{k}, l_{1},\ldots, l_{n})$ _Z then Z_$y=l_{i}$ _Z or Z_$y=\lnot l_{i}$ _Z for every value of Z_$s_{1}\ldots s_{k}$ _Z. In other words, each select input propagates exactly one of the latches to the output.
- Every latch node is propagated to the output: Z_$y=l_{i}$ _Z or Z_$y=\lnot l_{i}$ _Z for all Z_$i$ _Z and appropriate Z_$s_{1}\ldots s_{k}$ _Z.

The logic that controls RAM writes is shown in Fig. 6. It consists of decoders driving 2:1 muxes that select between the write-data and the latch output. The muxes drive the latch inputs and their select signal is the write-enable signal, denoted by Z_$we_{i}$ _Z. Once the latches that comprise the register file are known, cut matching can give us these muxes. Our algorithm then computes the BDDs for each write-enable signal using the intersection of combinational fan-in cones. The following properties are then verified^{13}:

- Each write-enable signal is satisfiable: Z_$we_{i}\ne 0$ _Z.
- No two write-enable signals are simultaneously satisfiable: Z_$we_{i}\land we_{j}=0$ _Z if Z_$i\ne j$ _Z.

Fig. 6. RAM write-logic: Z_$we_{i}$ _Z is the write-enable signal for word Z_$i$ _Z and Z_$wd_{i}$ _Z is the data to be written to word Z_$i$ _Z.

If these properties are satisfied, the set of gates that comprise the latch inputs, muxes and common support nodes are identified as the write-logic.^{14}

One RAM structure, a 32x32b register file with two read ports and 1 write port, was detected in the RISC FPU.

We use the term multibit register to denote a set of 1-bit registers whose values are updated in tandem.

One example of a multibit register is shown in Fig. 7. Each cycle either one of three different values: Z_$v1[7:0]$ _Z, Z_$v2[7:0]$ _Z or Z_$v3[7:0]$ _Z or the current value of the register Z_$q_{7}\ldots q_{0}$ _Z is assigned to the register based on the conditions Z_$c_{1}$ _Z, Z_$c_{2}$ _Z and Z_$c_{3}$ _Z. A structure of this form can be detected using bitslice matching and aggregation to find the multibit multiplexer and then examining the fanouts and inputs of the multiplexer to detect the sequential elements around it.^{15}

Fig. 7. Register synthesis illustration.

39 multibit register elements were found in the *RISC FPU *.

The inference algorithms described in this paper operate independently. Therefore, it is possible that a particular gate in the netlist under analysis might be placed in multiple inferred modules. For example, in the *oc8051 * design, the RAM read-array consists of many muxes identified by the bitslice aggregation algorithms and the RAM analysis algorithm.

One idea would be to output all inferred modules and allow a human analyst to pick and choose the “correct” non-overlapping description of the circuit. While this may be a feasible option for small circuits, for some of the larger circuits, the inference tool produces several tens of thousands of modules. It would be infeasible for a human to look through all these modules and select a non-overlapping subset.

In this section, we investigate algorithmic techniques for generating a non-overlapping subset of inferred modules given the output of the portfolio. In particular, we would like to generate non-overlapping subsets that either (i) maximize coverage (measured by number of gates identified) or (ii) minimize the number of inferred modules while meeting a coverage target. The former objective is desirable because it attempts to identify as many gates as possible. The latter is interesting because we expect that an inference output with fewer modules while meeting the required coverage target would be easier to understand from a human analysts' point of view.

At a high-level, our solution involves formulating a binary integer linear program (BILP; sometimes called a Zero-One ILP) that selects a non-overlapping subset of modules that optimizes for the desired target metric. We describe the formulation of the ILP in the following subsection.

The basic formulation requires one binary variable for each inferred module. Suppose there are a total of Z_$M$ _Z modules, then the formulation has Z_$M$ _Z binary variables Z_$x_{1}, x_{2}, x_{3},\ldots, x_{M}$ _Z. Setting variable Z_$x_{i}=1$ _Z denotes that module Z_$i$ _Z will be selected for output, while Z_$x_{i}=0$ _Z means that module Z_$i$ _Z will be elided from the output.

Consider an arbitrary element Z_$g_{k}$ _Z from the netlist being analyzed. Suppose this element Z_$g_{k}$ _Z is covered by inferred modules Z_${k_{1}},{k_{2}},\ldots,{k_{l}}$ _Z. To represent the requirement that element Z_$g_{k}$ _Z can be covered by only one of these modules in the final output, we introduce a constraint of the following form. $$x_{k_{1}}+x_{k_{2}}+\cdots+x_{k_{l}}\leq 1$$

There are as many constraints as there are elements in the netlist that are covered by multiple modules.

The objective of maximizing coverage is encoded in a straightforward manner. Let the “size” (*i.e. *, the number of elements covered by) module Z_$i$ _Z be Z_$S_{i}$ _Z. Then the objective function is:$$maximize~\sum_{i=1}^{M}x_{i}\cdot S_{i}$$

This formulation minimizes the number of output modules while introducing a new constraint that ensures that a certain coverage target is met. We retain the same variables as the previous formulation (described in Section IV-A-I) and use the same constraints to encode the selection of non-overlapping modules (see Section IV-A-II). The objective is as follows.$$minimize\sum_{i=1}^{M}x_{i}$$

We also need to introduce a new constraint that encodes the fact that the coverage target of Z_$C_{t}$ _Z elements must be met. This is done by adding the following constraint to the ILP. $$\sum_{i=1}^{M}x_{i}\cdot S_{i}\geq C_{t}$$

To motivate the need for the “sliceable” ILP formulation consider the example shown in Fig. 8. One box shows a 5-bit 2:1 MUX aggregated using the common select signal. This box partially overlaps with a RAM module because two of the bitslices in the 2:1 MUX are also included in the RAM module. Overlaps such as this occur because the bitslice aggregation algorithms are “greedy” in the sense that these inferred modules are created with the maximum number of bitslices matching the common select signal.

Fig. 8. Illustration of need for the “sliceable” formulation.

The basic formulation uses a single binary variable to either select or discard an inferred module. Therefore, the formulation will result in either the 2:1 MUX or the RAM being included in the final output but not both. This is suboptimal because there is a third option. The 2:1 MUX can be “sliced” to include the 3 bitslices that don't overlap with the RAM and then the entire RAM can be included. In this section we develop an ILP formulation that allows modules to be “sliced” in this manner.

Inferred modules are grouped into two categories. Modules like muxes and decoders which can be split up into independent bitslices are considered “sliceable”. If such a module has Z_$n$ _Z slices, it is modelled in the ILP with Z_$n+1$ _Z binary variables: Z_$x_{i_{0}}, x_{i_{1}},\ldots, x_{i_{n}}$ _Z. Variable Z_$x_{i_{j}}$ _Z where Z_$j\geq 1$ _Z represents whether slice Z_$j$ _Z of module Z_$i$ _Z is selected for output. Variable Z_$x_{i_{0}}$ _Z is a special variable introduced for technical reasons. It represents where module Z_$i$ _Z itself (*i.e. *, any slice in module Z_$i$ _Z) is selected for output. In the example shown in Fig. 8, suppose the 5-bit multiplexer is module Z_$i$ _Z, then MUX bitslices 1, 2, 3, 4 and 5 will be represented by variables Z_$x_{i_{1}}$ _Z, Z_$x_{i_{2}}$ _Z, Z_$x_{i_{3}}$ _Z, Z_$x_{i_{4}}$ _Z and Z_$x_{i_{5}}$ _Z respectively.

Modules which are not “sliceable”, for example: counters and RAMs, are represented as before with a single binary variable Z_$x_{i}$ _Z which determines whether the entire module is selected for output or discarded.

The formulation in Section IV-A-II expressed the fact that if a gate is covered by Z_$l$ _Z different modules, no more than one of these modules could be selected for output. Here, we would like to express the same but at the finer granularity of slices rather than modules. For this it is necessary to assign the elements included in a module to its component slices.

Define the following function Z_$Var(g_{k}, i)$ _Z that maps an element Z_$g_{k}$ _Z contained in module Z_$i$ _Z to the ILP variable that represents the slice that Z_$g_{k}$ _Z is contained in. $$Var(g_{k}, i)=\cases{x_{i}&{if module $i$ is unsliceable}\cr x_{i_{j}}&{if $g_{k}$ is contained only in}\cr &{slice $j$ of module $i$}\cr x_{i_{0}}&{otherwise}\cr}$$

The intuition here is that for a sliceable module, elements which are contained in exactly one slice are mapped to that slice. Elements which are contained in more than one slice, are mapped to the variable Z_$x_{i_{0}}$ _Z which is the special variable that represents the entire module. Returning to the example in Fig. 8, the gates which are inside the boxes labelled “MUX bitslice Z_$j$ _Z” will be mapped to variable Z_$x_{i_{j}}$ _Z. The inverter however, is “part of” all bitslices, so it is mapped to Z_$x_{i_{0}}$ _Z.

As before, suppose element Z_$g_{k}$ _Z is covered by inferred modules Z_${k_{1}},{k_{2}},\ldots,{k_{l}}$ _Z. We add the following constraint.$$Var(g_{k}, k_{1})+Var(g_{k},{k_{2}})+\cdots+Var(g_{k},{k_{l}})\leq 1$$

Consider a gate that is contained within the box labelled “MUX Bitslice 4” in Fig. 8. The specific constraint introduced by a gate inside “MUX Bitslice 4” will be Z_$x_{i_{4}}\!+\! x_{j}\!\leq\! 1$ _Z. This tells the solver that either bitslice 4 or the RAM can be selected for output but not both. Unlike in the basic formulation, we are not restricting the selection of the other bitslices in the MUX.

For each sliceable module, we would like to specify that if any individual slice is selected, gates that are common to more than one slice are also selected. This leads to constraints of the following form.$${\rm forall}~1~\leq j~\leq n:~x_{i_{0}}-x_{i_{j}}\geq 0$$

In the notation above, module Z_$i$ _Z has Z_$n$ _Z slices and is modelled in the ILP using the variables Z_$x_{i_{0}}, x_{i_{1}},\ldots, x_{i_{n}}$ _Z.

We would also like to specify that each module contains a minimum number of slices to avoid creating very small modules. This is done using a constraint of the form:$$\sum_{j=1}^{n}x_{i_{j}}-MinSlices\cdot x_{i_{0}}\geq 0$$

Z_$n$ _Z is the number of slices in module Z_$i$ _Z.^{16} All results are shown in this paper are with Z_$MinSlices=2$ _Z.

The objective function to maximize coverage is similar to that presented in Section IV-A-III with the difference that we have to count “sizes” on a per-slice basis. Define the size function as follows.$$Size(x)=\Big\vert\big\{g_{k}~\vert~Var(g_{k}, i)=x~{\rm for~some}~i\big\}\Big\vert$$

Clearly, Z_$Size(x)$ _Z counts the number of elements covered by the variable Z_$x$ _Z. Given Z_$Size(x)$ _Z the objective function can be derived in a straightforward manner by weighting each variable with its corresponding size. $$maximize~\sum_{{\rm variable}~x}x\cdot Size(x)$$

Returning to the example in Fig. 8, the solver can maximize coverage by setting Z_$x_{i_{0}}$ _Z, Z_$x_{i_{1}}$ _Z, Z_$x_{i_{2}}$ _Z, Z_$x_{i_{3}}$ _Z and Z_$x_{j}$ _Z to 1 and Z_$x_{i_{4}}$ _Z and Z_$x_{i_{5}}$ _Z to zero. This satisfies all the constraints we have described and selects bitslices 1, 2 and 3 of the MUX and the entire RAM.

The formulation that minimizes the number of inferred modules while meeting the coverage target Z_$C_{t}$ _Z again requires the addition of the following constraint. $$\sum_{{\rm variable}~x}x\cdot Size(x)\geq C_{t}$$

The following function returns the representative variable for a module Z_$i$ _Z. The representative variable determines whether a module is selected for output.$$rep(i)=\cases{x_{i}&{if module $i$ is not sliceable}\cr x_{i_{0}}&{if module $i$ is sliceable}\cr}$$

In the example shown in Fig. 8, the representative variables for the 5-bit multiplexer and RAM are Z_$x_{i_{0}}$ _Z and Z_$x_{j}$ _Z respectively.

Assuming the total number of modules is Z_$M$ _Z and that they are numbered from 1 to Z_$M$ _Z, the objective function is now given by the following equation.$$minimize~\sum_{i=1}^{M}rep(i)$$

We now present a detailed evaluation of our algorithms.

We developed an inference tool using the C++ and Python programming languages that implement the algorithms described in this paper. The tool takes as input a synthesized verilog netlist, analyzes it and outputs an abstracted netlist with the inferred components. The tool uses the CU Decision Diagram (CUDD) Package version 2.4.2 for the BDD-based analyses ^{[23]}, and MiniSat version 2.2 for satisfiability checking ^{[6]}. DepQBF ^{[16]} was used as the QBF solver and IBM CPLEX version 12.5 was the ILP-solver.

Experiments were performed on an Intel®Xeon®E31230 CPU clocked at 3.20 GHz with 32 GB of RAM. One set of results are shown for eight netlists. Details of these netlists are shown in Table II. All the designs were synthesized using an IBM/ARM cell library for a 45 nm SOI process. This paper also shows inference results on a large highly-optimized SoC design consisting of more than 375,000 combinational elements. A case study describing our analysis of this test article is given in Section V-C. Finally, we describe a case study where we inject hardware trojans into two of the test articles from Table II and discuss how our algorithms would aid an analyst detect these trojans.

Table III shows the modules identified and overall coverage obtained using our inference algorithms. Coverage is measured as a percentage of gates in the design which are covered by inferred modules. The table also shows information about the netlists being analyzed, the number of inferred modules of various types and the execution time of the tool.

TABLE III Coverage Results

For each test article, we show two rows. The white row shows the number of modules obtained before overlap resolution. This means that for the results shown in the white rows, each gate/flip-flop/latch in the test article may be placed into multiple different inferred modules. These results are directly comparable to the results presented in ^{[26]}. The shaded rows show the results after overlap resolution (Section IV) has been performed. In this case, each gate/latch/flip-flop is placed in atmost one inferred module. The process of overlap resolution necessarily involves a small loss in coverage but we see from the results shown that the loss is quite small.

For the three biggest netlists, coverage is above 70% and reaches up to 93% for the 16-bit MIPS CPU. These netlists all have a large number of replicated bitslices in the datapath which are captured well by the bitslice identification and aggregation algorithms. In contrast, the smaller netlists have a significant fraction of gates devoted to irregular control logic, which is hard to identify in a fully automated solution.

Both the execution time and memory requirements posed by the analysis tool are very reasonable. The maximum execution time among this set of designs is a little more than three minutes and the maximum resident set size is 4.1 GB. The most computationally-expensive algorithm in our toolbox is the counter analysis.

Table IV compares the basic ILP formulation (Section IV-A) with the sliceable ILP formulation (Section IV-B). Recall that the basic formulation can only select or discard an entire module, while the sliceable formulation can select or discard a subset of the bitslices in a multibit module. We expect that the sliceable formulation will always have the same or better coverage than the basic formulation. However, the trade-off here is again that the sliceable formulation will somtimes tend to choose a few smaller modules over one big module. Results in Table IV are in keeping with these expectations.

TABLE IV Comparing the Sliceable and Basic Formulations

We present a case study of algorithmic reverse engineering of large, realistic SoC. This SoC consists of 375090 combinational elements, 34318 latches and 62 and 94 inputs and outputs respectively. We describe the three part strategy we used to analyze this design in the rest of this subsection.^{17} Besides the gate-level netlist, we were also given a datasheet for the SoC. The datasheet listed the seven constituent cores of the SoC and provided a brief description of the high-level functionality of each core.

The SoC in its raw form contains many redundant combinational elements such as delays, buffers and paired inverters which were inserted presumably for electrical reasons. These elements result in the inference of many functionally equivalent modules with slightly different module boundaries and adversely affect computational performance and scalability. Therefore, our first step was to perform structural logic simplification and eliminate buffers, delays, paired inverters and a few other structurally equivalent gates. This reduced the number of combinational elements in the SoC from 375090 to 168730, a reduction of about 55%!

Even after logic simplification, we found that the computationally expensive analysis algorithms—counter and shift register detection—timed out on the complete design. Although these inferred modules are small, they are important in gaining insight into the working of the design. Therefore, the second step in our analysis of the SoC was to improve analysis scalability by partitioning the SoC into its constituent cores.

The datasheet of the SoC informed us that the SoC had seven constituent cores. The SoC had individual reset inputs for each of these cores and we used these inputs in partitioning the SoC into its constituent parts. The partitioning algorithm marks each latch with all the reset inputs that are in its combinational fan-in cone. The union of the set of all latches marked with a module's reset input and all the gates in their respective fan-in cones yields the module partitioning.

The details of the partitioning are shown in Table V. Note that a very small number of gates (176 or 0.1%) of gates are placed into more than one module. We assert that this discrepancy can easily be resolved by a human analyst during a later stage of the investigation. About 5% of the gates are not placed in any partition. We believe these gates correspond to an inter-core interconnect mentioned in the datasheet.

TABLE V BigSoC Partition Information

Results of analyzing the partitions as well as the entire SOC are shown in Table VI. We note that the coverage is between 62% and 88%. The VGA module contains a Z_$12000+$ _Z gate “framebuffer read” structure that was detected using a design-specific algorithm. These results demonstrate that our inference algorithms are effective on very realistic large SoC designs. The computational requirements for the analysis are reasonable and the entire analysis can be performed in slightly over two hours on a contemporary midrange server CPU.

TABLE VI Coverage Results on BigSoC Partitions

In order to demonstrate how our reverse engineering algorithms can aid trojan detection, we now present a case study where we analyze trojan-injected versions of two of the test articles studied above. Our goal here is demonstrate how inferences from our analysis algorithms can aid a human analyst who is trying to detect the presence of hardware trojans in a gate-level netlist. As stated previously, we assume that the analyst does not have access to the RTL source code and/or known good chips and has to rely on manual and algorithmic analysis of the gate-level netlist to detect malicious behavior.

We injected trojans into the *oc8051 * and *eVoter * test articles. A comparison of the original and the trojan-inserted versions of these test articles is shown in Table VII.

TABLE VII Details of Trojan-Inserted Designs

In the case of the *eVoter *, the trojan is activated a by secret seven key sequence and allows selection of a specific candidate. All subsequent votes now go to this candidate. The trojan can be deactivated by pressing the secret key sequence again. In this case, the trojan is a backdoor that can be used to compromise the voting machine hardware.

In the case of the *oc8051 *, the trojan circuitry is activated when an XOR instruction is a repeated 5 times in a row. Once the trojan is activated all outputs from the ALU to the accumulator are set to zero. In other words, the trojan here is a kill-switch activated by a rare sequence of instructions.

Table VIII summarizes the results of the inference algorithms on the trojan-inserted designs. We chose to show the results before and after overlap resolution because the resolution algorithm may discard modules that provide insight into the trojan because these modules overlap with other inferred modules.

TABLE VIII Trojan Analysis Results and Comparison

In the case of the *eVoter * trojan, we see that several additional decoders and demultiplexers are inferred. These modules correspond to the logic in the trojan that matches the specific secret key sequence which activates the trojan. Further, we see two additional muxes and one additional multibit register. The mux and multibit register here are especially prominent because there are only a few of these modules in the design. In fact, the additional modules here precisely correspond to the logic that overrides the user input button number (i.e., user vote) with the secret trojan/backdoor input.

A human analyst analyzing the inferred modules with no prior knowledge of the trojan is likely to have noticed this mux that selects either the user input button number or a multibit register. Further, the analyst would have noticed several decoders, which are part of a state machine, and that these decoders are also driven by the input button number. Combining this with some manual analysis of the state machine and discovering that the state machine drives the select input of this mux would very likely have led to the discovery of the hardware trojan.

The most important additional module discovered by the algorithms for the *oc8051 * trojan is a counter. This is in fact the counter which counts the number of consecutive XOR instructions being executed by the ALU. The tool also discovered a gating function module that zeros out the ALU output. This module is enabled by a set of decoders that are driven by output of the counter.

Reviewing the inference results from the point of view of an analyst with no prior knowledge of the trojan, the following steps are necessary to discover the trojan/kill-switch. First, the analyst needs to discover the accumulator in the 8051. Our tool can help discover the accumulator because the ALU adder and subtractor outputs are connected to the accumulator. The next key step in detecting the trojan is combining knowledge of which circuit registers form the accumulator along with the gating function inferred module that zeros out the accumulator. The third key step is piecing together the counter output and decoders that enable this gating function. Even if it is not entirely clear what event is being counted, the fact that a count reaching a specific value triggers a kill-switch which results in the accumulator being permanently zeroed out is sufficient to determine the presence of a trojan. We assert that these inference steps are fairly straightforward for an analyst if given the help of our tools. Without help from an algorithmic reverse engineering tool, the analyst would have to resort to examining each of the several thousand gates and registers in this designs, making detection unlikely.

In this section, we discuss some of limitations and areas for potential improvement in our tools. We also provide a detailed comparison with our previous work which this paper builds on.

Some of the inferred modules detected by the tool, such as decoders and demultiplexers are somewhat small and cover tens of gates leading to a moderately large number of such modules in the output. Due to this, a human analyst would need to spend more time looking at each of these inferred modules. At first sight, this appears to be a major limitation. However, it is important to note that even with these small modules, the number of inferred modules is at least an order magnitude and usually a few orders of magnitude fewer than the number of gates and registers present in the article being analyzed. As we pointed out in the discussion of the trojan injected in *oc8051 *, this results in a very significant reduction in the workload for an analyst.

Furthermore, if design-specific information is available about the types of modules expected to be present in the design, our algorithms can be easily extended to detect such modules. Two examples are shown in this paper. The first is the framebuffer-read structure in the VGA module of *BigSoC *. In this case, we designed the algorithm to detect this structure knowing that a VGA controller and framebuffer were present in the design.

A second example is our detection of the ALU in *oc8051 *. Detecting an ALU requires knowledge of the exact functions implemented by an ALU such as add, subtract, not, negate, and, nand, or, xor, xnor etc. These vary from design to design. Moreover, the number of ALU operation specifier bits, the number of input operands and their bitwidths also needs to be known or derived. These factors make it hard to write a completely general ALU detection algorithm. However, in the case of the *oc8051 *, analyzing the instruction set gave us information about the functions performed by the ALU as well as the width of each ALU input. In this case, we were able to use QBF-based module matching and word identification to precisely identify the ALU in the *oc8051 * design. Building such a large library of high-level components is an important topic for future work.

Finally, we wish to point out that it is actually advantageous for the tool to detect small “building-block” type of modules in the trojan detection scenario. Small modules like decoders, counters, multiplexers and gating functions are the building blocks from which higher-level functionality is derived. Since trojans can be implemented in a variety of different ways, detecting these building blocks is a more promising approach than designing algorithms that detect high-level modules that correspond to trojans. Such algorithms will likely be stymied by small differences in implementation of the trojan.

Our algorithmic inference tool can automatically reverse engineer between 45% and 93% of the gates in chip. This still leaves a significant number of gates that need to be reverse engineered to completely understand the chip's functionality. In the rest of the section, we discuss some ways of reverse engineering these gates.

It is important to note that placing 100% of the gates and registers in a design into inferred modules is not necessary for trojan detection. As we showed in Section V-D, identifying a few key modules in the trojan is sufficient to alert an analyst to potentially malicious behavior. And because our algorithms can infer a rich library of logical building blocks, we assert that a significant part of almost any trojan would be covered by the algorithms presented here.

A human analyst may extend the analysis tool with bitslices and aggregation algorithms specific to the chip being analyzed. We used this technique to identify the VGA frame buffer structure in the BigSoC design.

Besides fully identified modules, the tool can also be made to output “candidate” modules generated by common signal aggregation of “unknown” bitslices (Section II-B). A human analyst can look at the generated modules and try to understand their functionality, for example, by simulating with random inputs. Analyzing these modules is easier than analyzing the entire chip because: (i) the modules only have a fews tens or hundreds of gates and (ii) the modules have regularity and structure unlike the full netlist.

We can derive useful information about the functionality of unidentified gates using the output of the tool. Two of the counters identified in the *router * are actually head and tail pointers which index into a FIFO. Knowing these are counters helped understand the functionality of the indexing structure. Another case is of structures that do not have a clean interconnection pattern but have replicated bitslices that can be detected using cut-based Boolean matching.^{18}

The techniques in this paper focuses entirely on “static” analysis of the netlist. Simulation of the netlist with carefully constructed test vectors is a form of dynamic analysis that can provide valuable information. For instance, one conceivable way of detecting an FFT co-processor is to construct a test program executing FFTs in a loop, simulating its execution and observe where the (known) operands and results of the transform show up. We are working on such algorithms.

This paper introduced a portfolio of algorithms for reverse engineering gate-level netlists. It builds on our previous work ^{[13]}, ^{[26]} in this area. The work in ^{[26]} introduced bitslice matching and aggregation and provided a brief overview of the algorithms for counter, shift register and RAM detection. This work adds new algorithms based on analyzing nodes with common support (Section II-E), the multibit register analysis (Section III-D) and the ILP formulation to resolve overlapping output modules (Section IV). This paper has also integrated the functional word propagation algorithm (Section II-C) and QBF-based module matching algorithm (Section II-D) from ^{[13]}. These algorithms have proven to be more effective than the structural word propagation and BDD-based module matching algorithms presented in ^{[26]}. This paper also expanded on the descriptions of the algorithms for detecting counters (Section III-A), shift registers (Section III-B) and RAMs (Section III-C). The evaluation of our algorithms (Section V) in this paper is much more detailed. In particular, we believe that the detailed analysis of the BigSoC design (Section V-C) and the partitioning algorithms used in making the analysis of BigSoC tractable significant contributions of this paper. The trojan detection experiments from Section V-D which demonstrate the feasibility of trojan detection aided by algorithmic reverse engineering are also an important novel contribution of this paper.

Integrated circuits are now designed and fabricated in a globalized and multi-vendor environment making them vulnerable to malicious design changes and hardware trojans. Algorithmic reverse engineering can mitigate these risks by helping detect malware and verify the integrity of critical ICs.

The key challenge in reverse engineering digital circuits is generating meaningful module boundaries given a very large unstructured netlist of gates. In this paper, our main contribution is a portfolio of algorithms for reverse engineering which: (i) find module boundaries for a variety of combinational and sequential components and (ii) functional analyses that verify the behavior of these modules. Experiments showed that the functionality of 45% to 93% of the gates in a netlist may be automatically inferred using our algorithms. We also demonstrated that our algorithms achieve 68% coverage on a large highly-optimized SoC consisting of over 375,000 gates. We also demonstrated that these algorithms are very effective in aiding a human analyst detect hardware trojans in an unstructured netlist.

- Note that 100% identification will not be possible because of the focus on datapath components. This is not a serious limitation as discussed in Sections V-D and VI.

- We define a bitslice as a Boolean function with one output and a small number of inputs that is replicated to construct multibit datapath operators.

- 2QBF is the problem of evaluating a Quantified Boolean Formula (QBF) with two levels of quantification
^{[21]}.

- Assuming the decoder outputs are active-high. The case when the decoders outputs are negated is handled using a symmetric algorithm.

- Although we verified that the population count algorithm works on artificially constructed circuits with popcnt modules in them, we could not find any population counters in the test circuits we experimented with.

- For clarity of presentation the rest of this section focuses on up counters. Our implementation uses symmetric techniques to detect down counters.

- Given Boolean functions Z_$f$ _Z and Z_$g$ _Z, Z_$cofactor(f, g)$ _Z is the function obtained when Z_$f$ _Z is evaluated over the restricted domain specified by Z_$g=1$ _Z.

- The analysis handles each bit output of the array independently so sets of latches with common select inputs (read addresses) are aggregated to form an array with multibit inputs/outputs.

- This presentation assumes the write-enable is signal is active high, but it could also be active-low in which case the properties are modified appropriately. We determine the polarity of the write-enable signal by examining which of the mux inputs is connected to the latch output.

- We note that the analysis is unable to determine the ordering of the bits in inputs and outputs of the RAM.

- As in the case of the RAM identification, the analysis is unable to determine the ordering of the bits in the multibit register. In some cases, we were able to infer the ordering of bits by seeding the symbolic word propagation algorithm with ordered words and checking whether one of the propagated words matched the register outputs. Note ordered words can be inferred from aggregation algorithms for adders and subtractors.

- Note adding the constraint Z_$\sum_{j=1}^{N}x_{i_{j}}\geq MinSlices$ _Z is incorrect. This requires every module to have Z_$MinSlices$ _Z slices selected. What we want is:
*if*a module is selected, it must have at least Z_$MinSlices$ _Z slices in it.

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Pramod Subramanyan is currently pursuing the Ph.D. degree with the Department of Electrical Engineering, Princeton University. He received the M.Sc. (Engg.) degree from IIS and the B.E. degree in electronics and communication engineering from the R. V. College of Engineering. His research interests are in formal methods, computer security, and computer architecture.

Nestan Tsiskaridze is a Post-Doctoral Research Scholar and Visiting Assistant Professor with the Department of Computer Science, University of Iowa. She currently works with Prof. C. Tinelli on developing new satisfiability modulo theories (SMT) solving techniques to aid checking of software security properties, and on enhancing the CVC4 SMT solver with the new techniques. She was a Post-Doctoral Research Associate with the Department of Electrical Engineering, Group of Prof. Sharad Malik, Princeton University. She received the Ph.D. degree in computer science from the Department of Computer Science, University of Manchester, under the supervision of Prof. A. Voronkov.

Wenchao Li is a Post-Doctoral Fellow with the Computer Science Laboratory, SRI International, Menlo Park. He received the M.S. and Ph.D. degrees in electrical engineering and computer sciences from UC Berkeley. His research interests are formal methods and machine learning, with a focus on building and analyzing dependable systems.

Adrià Gascón is in the last stage of his doctoral studies from the Technical University of Catalonia, where he worked on unification theory, term compression, rewriting theory, and tree automata theory. His current research interests include program synthesis, distributed algorithms, and unification theory.

Wei Yang Tan is currently pursuing the M.Sc. degree in computer science with UC Berkeley. He was with the DSO National Laboratories, Singapore, as a Research Engineer. He received the B.Eng. degree in computer engineering from the National University of Singapore in 2008. His research interests are in applying formal methods to problems in embedded systems and computer security.

Ashish Tiwari received the B.Tech. and Ph.D. degrees in computer science from IIT, Kanpur, and the State University of New York at Stony Brook in 1995 and 2000, respectively. He is currently a member of the formal methods group in the Computer Science Laboratory, SRI International. His research interests are in automated deduction, decision procedures, program analysis, and formal methods for analysis and verification of hybrid system models of embedded software, control systems, and biological systems.

Natarajan Shankar has been a Staff Scientist with the SRI Computer Science Laboratory since 1989. He received the Ph.D. degree in computer science from the University of Texas at Austin in 1986. His interests are in the study of formal methods for the specification and verification of hardware and software, in automated deduction, and in computational logic. He was the Boyer-Moore theorem prover to check proofs of various metamathematical theorems, including Gdel's incompleteness theorem and the Church-Rosser theorem. His book *Metamathematics, Machines, and Gödel's Proof * was published with Cambridge University Press, in 1994. He has contributed to the foundation of linear logic and the theory of proof search in nonclassical logics. He has co-developed the design and implementation of the PVS verification system and has written and lectured extensively on it. He led the development of the Symbolic Analysis Laboratory and is a co-designer of the ICS decision procedures and has contributed to its underlying theory and implementation of the ICS and Yices solvers for satisfiability modulo theories. He is currently developing the evidential tool bus semantic framework for combining reasoning tools, and the probabilistic consistency engine for learning and inferring probabilistic assertions. He is a Past Chairman of the IFIP Working Group 2.3 on Programming Methodology. He is a member of editorial boards of the *Journal of Theoretical Computer Science *, the *Journal of Automated Reasoning, Logical Methods in Computer Science *, and the *Journal of Formalized Reasoning *.

Sanjit A. Seshia is an Associate Professor with the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. He received the M.S. and Ph.D. degrees in computer science from Carnegie Mellon University and the B.Tech. degree in computer science and engineering from IIT, Bombay. His research interests are in dependable computing and computational logic, with a current focus on applying automated formal methods to problems in embedded systems, electronic design automation, computer security, and program analysis. His Ph.D. thesis work on the UCLID verifier and decision procedure helped pioneer the area of satisfiability modulo theories (SMT) and SMT-based verification. He is the co-author of a widely-used textbook on embedded systems. His awards and honors include the Presidential Early Career Award for Scientists and Engineers from the White House, the Alfred P. Sloan Research Fellowship, and the School of Computer Science Distinguished Dissertation Award at Carnegie Mellon University.

Sharad Malik received the B.Tech. degree in electrical engineering from IIT, New Delhi, in 1985, and the M.S. and Ph.D. degrees in computer science from the University of California, Berkeley, in 1987 and 1990, respectively. Currently, he is the George Van Ness Lothrop Professor of engineering with Princeton University and the Chair of the Department of Electrical Engineering. His research focuses on design methodology and design automation for computing systems. His research in functional timing analysis and propositional satisfiability has been widely used in industrial electronic design automation tools. He received the DAC Award for the most cited paper in the 50-year history of the conference in 2013, the CAV Award for fundamental contributions to the development of high-performance Boolean satisfiability solvers in 2009, the ICCAD Ten Year Retrospective Most Influential Paper Award in 2011, the Princeton University Presidents Award for Distinguished Teaching in 2009, as well as several other best paper and teaching awards.