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Lightweight Detection and Recovery Mechanisms to Extend Algorithm Resiliency in Noisy Computation

Sek Chai, David Zhang, Jingwen Leng, Vijay Janapa Reddi

In Second Workshop on Near-threshold Computing (co-located with ISCA), 2014.


The intrinsic robustness of an algorithm and architecture depends highly on the combined ability tolerate noise. In this paper, we present an alternative approach for energy reduction for near threshold computing based on a statistical modeling of computational noise induced from noisy memory and non-ideal interconnects. We present this approach as a complement to the standard approximate computing approaches. We show results of the lightweight error checks and recovery based on several design considerations on data value speculation.