LPQ-SAM: A Low-Power Quality Scalable Approximate Multiplier

摘要

Approximate computing allows compromising accuracy to attain energy and performance efficient designs. However, the accuracy requirements of many applications change on runtime and it has been often observed that traditional approximate hardware tends to either provide unacceptable results or leads to an unnecessary computational effort. Quality scalable configurations can overcome these limitations. With the same motivation, we propose a low-power quality scalable approximate multiplier (LPQ-SAM) in this paper. This low power multiplier has various accuracy reconfigurable modes, including an accurate one and thus, it can be used for both error-resilient and exact applications. LPQ-SAM is exhaustively tested for different error metrics and it has been observed that in the approximate mode, it provides up to 19% and 55% power reduction compared to the exact Booth and Wallace multipliers, respectively. For illustration purposes, we demonstrated the effectiveness of LPQ-SAM on a real-time application, i.e., image masking.