Reducing Energy at the Minimum Energy Operating Point Via Statistical Error Compensation

This paper demonstrates that statistical error compensation reduces the energy consumption Emin at the minimum energy operating point (MEOP), which is known to occur in the subthreshold regime. In particular, the impact of algorithmic noise-tolerance (ANT) [1], in conjunction with frequency overscaling (FOS) and voltage overscaling, is studied in the context of an eight-tap finite impulse response (FIR) filter in a 45-nm CMOS process. At the nominal process corner and using low-V_t devices, we show that the ANT-based FIR filter achieves 20%-47% reduction in Emin and a 1.8×-2.25× increase in the frequency of operation over a conventional (error free) filter operating at its MEOP. This result is achieved via the ability of ANT to compensate for a precompensation error rate of 70%-85%. The use of high-V_t devices reduces Emin by 10%. This is due to the reduced effectiveness of FOS and increased sensitivity of delay to voltage variations. In the presence of process variations, the ANT-based FIR filter reduces Emin by 54% over a transistor up-sized design while meeting a fixed throughput constraint, and a parametric yield of 99.7%.