Adaptive low-energy techniques in memory and digital signal processing design
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As semiconductor technology continues to scale, energy-efficiency and power consumption have become the dominant design limitations, especially, for embedded and portable systems. Conventional worst-case design is highly inefficient from an energy perspective. In this dissertation, we propose techniques for adaptivity at the architecture and circuit levels in order to remove some of these inefficiencies. Specifically, this dissertation focuses on research contributions in two areas: 1) the development of SRAM models and circuitry to enable an intra-array voltage island approach for dealing with large random process variation; and 2) the development of low-energy digital signal processing (DSP) techniques based on controlled timing error acceptance. In the presence of increased process variation, which characterizes nanometer scale CMOS technology, traditional design strategies result in designs that are overly conservative in terms of area, power consumption, and design effort. Memory arrays, such as SRAM-based cache, are especially vulnerable to process variation, where the penalty is a power and bit-cell increase needed to satisfy a variety of noise margins. To improve yield and reduce power consumption in large SRAM arrays, we propose an intra-array voltage island technique and develop circuits that allow for a cost-effective deployment of this technique to reduce the impact of process variation. The voltage tuning architecture makes it possible to obtain, on average, power consumption reduction of 24% iso-area in the active mode, and the leakage power reduction up to 52%, and, on average, of 44% iso-area in the sleep mode. Alternatively, bitcell area can be reduced up to 50% iso-power compared to the existing design strategy. In many portable and embedded systems, signal processing (SP) applications are dominant energy consumers. In this dissertation we investigate the potential of error-permissive design strategies to reduce energy consumption in such SP applications. Conventional design strategies are aimed at guaranteeing timing correctness for the input data that triggers the worst-case delay, even if such data occurs infrequently. We notice that an intrinsic notion of quality floor characterizes SP applications. This provides the opportunity to significantly reduce energy consumption in exchange for a limited signal quality reduction by strategically accepting small and infrequent timing errors. We propose both design-time and run-time techniques to carefully control the quality-energy tradeoff under scaled VDD. The basic philosophy is to prevent signal quality from severe degradation, on average, by using data statistics. We introduce techniques for: 1) static and dynamic adjustment of datapath bitwidths, 2) design-time and run-time reordering of computations, 3) protection of important algorithm steps, and 4) exploiting the specific patterns of errors for low-cost post-processing to minimize signal quality degradation. We demonstrate the effectiveness of the proposed techniques on a 2D-IDCT/DCT design, as well as several digital filters for audio and image processing applications. The designs were synthesized using a 45nm standard cell library with energy and delay evaluated using NanoSim and VCS. Experiments show that the introduced techniques enable 40~70% energy savings while only adding less than 6% area overhead when applied to image processing and filtering applications.