An Efficient Fault Injection Algorithm for Identifying Unimportant FFs in Approximate Computing Circuits

Abstract

Approximate Computing (AC) saves energy and improves performance by introducing approximation into computation in error-torrent applications. This work focuses on an AC strategy that accurately performs important computations and approximates others. In order to determine which calculations are unimportant, we propose a novel importance evaluation algorithm, in which the key idea is a two-step fault injection to extract the near-optimal set of unimportant flip-flops in the circuit. The proposed algorithm reduces the complexity of architecture exploration from an exponential order to a linear order with-out understanding the functionality and behavior of the target application program.