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

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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- $V_{\rm t}$ devices, we show that the ANT-based FIR filter achieves 20%–47% reduction in $E_{\min}$ and a $1.8\times$ – $2.25\times$ 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- $V_{\rm t}$ 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%.