According to Moore's law, the number of transistors in a chip doubles every 18 months. The increased transistor-count leads to increased power density. Thus, in modern circuits, power efficiency is a central determinant of circuit efficiency. With scaling, leakage power accounts for an increasingly larger portion of the total power consumption in deep submicron technologies (>40%). FinFET technology has been proposed as a promising alternative to deep submicron bulk CMOS technology, because of its better scalability, short-channel characteristics, and ability to suppress leakage current and mitigate device-to-device variability when compared to bulk CMOS. The subthreshold slope of a FinFET is approximately 60mV which is close to ideal. In this article, we propose a methodology for low-power FinFET based circuit synthesis. A mechanism called TCMS (Threshold Control through Multiple Supply Voltages) was previously proposed for improving the power efficiency of FinFET based global interconnects. We propose a significant generalization of TCMS to the design of any logic circuit. This scheme represents a significant divergence from the conventional multiple supply voltage schemes considered in the past. It also obviates the need for voltage level-converters. We employ accurate delay and power estimates using table look-up methods based on HSPICE simulations for supply voltage and threshold voltage optimization. Experimental results demonstrate that TCMS can provide power savings of 67.6% and device area savings of 65.2% under relaxed delay constraints. Two other variants of TCMS are also proposed that yield similar benefits. We compare our scheme to extended cluster voltage scaling (ECVS), a popular dual-V dd scheme presented in the literature. ECVS makes use of voltage level-converters. Even when it is assumed that these level-converters have zero delay, thus significantly favoring ECVS in time-constrained power optimization, TCMS still outperforms ECVS.
|Original language||English (US)|
|Journal||ACM Journal on Emerging Technologies in Computing Systems|
|State||Published - Jul 1 2009|
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Electrical and Electronic Engineering
- Linear programming