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This paper surveys state of the art low-power techniques for both single and multicore systems. Based on our proposed power management model for multicore systems, we present a classification of total power reduction techniques including both leakage and active power. According to this classification, three main classes are discussed: power optimization techniques within the cores, techniques for the interconnect and techniques applicable for the whole multicore system. This paper describes several techniques from these classes along with a comparison. For the whole multicore system, we focus on adaptive voltage scaling and propose a comprehensive taxonomy of adaptive voltage scaling techniques, while considering process variations.

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With the continued down-scaling of IC technology and increase in manufacturing process variations, it is becoming ever more difficult to accurately estimate circuit performance of manufactured devices. This poses significant challenges on the effective application of adaptive voltage scaling (AVS) which is widely used as the most important power optimization method in modern devices. Process variations specifically limit the capabilities of Process Monitoring Boxes (PMBs), which represent the current industrial state-of-the-art AVS approach. To overcome this limitation, in this paper we propose an alternative solution using delay testing, which is able to eliminate the need for PMBs, while improving the accuracy of voltage estimation. The paper shows, using simulation of ISCAS'99 benchmarks with 28nm FD-SOI library, that using delay test patterns result in an error of 5.33% for transition fault testing (TF), error of 3.96% for small delay defect testing (SDD), and an error as low as 1.85% using path delay testing (PDLY). In addition, the paper also shows the impact of technology scaling on the accuracy of delay testing for performance estimation during production. The results show that the 65nm technology node exhibits the same trends identified for the 28nm technology node, namely that PDLY is the most accurate, while, TF is the least accurate performance estimator.