IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems (TPDS) is a scholarly archival journal published monthly. Parallelism and distributed computing are foundational research and technology to rapidly advance computer systems and their applications. Read the full scope of TPDS.
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From the February 2018 Issue
Neurostream: Scalable and Energy Efficient Deep Learning with Smart Memory Cubes
By Erfan Azarkhish, Davide Rossi, Igor Loi, and Luca Benini
High-performance computing systems are moving towards 2.5D and 3D memory hierarchies, based on High Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) to mitigate the main memory bottlenecks. This trend is also creating new opportunities to revisit near-memory computation. In this paper, we propose a flexible processor-in-memory (PIM) solution for scalable and energy-efficient execution of deep convolutional networks (ConvNets), one of the fastest-growing workloads for servers and high-end embedded systems. Our co-design approach consists of a network of Smart Memory Cubes (modular extensions to the standard HMC) each augmented with a many-core PIM platform called NeuroCluster. NeuroClusters have a modular design based on NeuroStream coprocessors (for Convolution-intensive computations) and general-purpose RISC-V cores. In addition, a DRAM-friendly tiling mechanism and a scalable computation paradigm are presented to efficiently harness this computational capability with a very low programming effort. NeuroCluster occupies only 8 percent of the total logic-base (LoB) die area in a standard HMC and achieves an average performance of 240 GFLOPS for complete execution of full-featured state-of-the-art (SoA) ConvNets within a power budget of 2.5 W. Overall 11 W is consumed in a single SMC device, with 22.5 GFLOPS/W energy-efficiency which is 3.5X better than the best GPU implementations in similar technologies. The minor increase in system-level power and the negligible area increase make our PIM system a cost-effective and energy efficient solution, easily scalable to 955 GFLOPS with a small network of just four SMCs.
Editorials and Announcements
- We are pleased to announce that Manish Parashar, a Distinguished Professor of Computer Science at Rutgers, The State University of New Jersey University, has been selected as the new Editor-in-Chief of the IEEE Transactions on Parallel and Distributed Systems starting in 2018.
- We are pleased to announce that Xian-He Sun, a Distinguished Professor of Computer Science at The Illinois Institute of Technology, has been selected as the new Associate Editor-in-Chief of the IEEE Transactions on Parallel and Distributed Systems starting in 2018.
- TPDS now offers authors access to Code Ocean. Code Ocean is a cloud-based executable research platform that allows authors to share their algorithms in an effort to make the world’s scientific code more open and reproducible. Learn more or sign up for free.
- According to Clarivate Analytics' 2016 Journal Citation Report, TPDS has an impact factor of 4.181.
- State of the Journal (January 2018)
- Editor's Note (December 2017)
- Editor's Note (January 2017)
- Editor's Note (January 2016)
- Editor's Note (January 2015)
- State of the Journal (January 2014)
- Editor's Note: EIC Farewell and New EIC Introduction (Dec 2013)
- Editor's Note: How to Write Research Articles in Computing and Engineering Disciplines by Ivan Stojmenovic
- Full Supplemental PDF of Editor's Note: How to Write Research Articles in Computing and Engineering Disciplines by Ivan Stojmenovic and Veljko Milutinovic (PDF)
- Special Issue on Trust, Security, and Privacy in Parallel and Distributed Systems (Feb 2014)
- Special Issue on Cloud Computing (June 2013)
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