So far, performance and reliability of circuits have been determined by worst-case characterization of silicon and environmental noise. As new deep sub-micron technologies exacerbate process variations and reduce noise margins, worstcase design will eventually fail to meet an aggressive combination of objectives in performance, reliability, and power. In order to circumvent these difficulties, researchers have recently proposed a new design paradigm: self-calibrating circuits. Design parameters (e.g., operating points) of selfcalibrating circuits are set by monitoring correctness of their operation, thus enabling to dynamically trade reliability for power or performance, depending on actual silicon capabilities and noise conditions.

In this paper, we study the problem of detecting errors caused by self-calibration of the supply voltage and frequency of an on-chip link. These errors are caused by operation at sub-critical voltage and may be numerous. We attack the problem with a coding technique. We also discuss an alternative approach using double sampling flip-flops. We stress the complementarity of the two approaches and show how they can be combined. Finally, we consider extending our work to computation. We give preliminary research directions on the detection of errors induced by self-calibration for an adder.