Reducing energy at the minimum energy operating point via statistical error compensation

This paper demonstrates that statistical error compensation reduces the energy consumption E_min 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 E_min 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 E_min 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 E_min by 54% over a transistor up-sized design while meeting a fixed throughput constraint, and a parametric yield of 99.7%.