Approximate Multipliers With Dynamic Truncation for Energy Reduction via Graceful Quality Degradation
Fabio Frustaci, Stefania Perri, Pasquale Corsonello, Massimo Alioto
Abstract
In approximate operators, dynamic truncation allows trading off energy and quality of computation at runtime. Although it exploits the specificity of the data being processed, its significant energy overhead over simple static truncation fundamentally limits its energy benefits. This brief describes a simple and efficient design methodology that reduces the energy consumption of dynamically truncated multipliers, based on a smart mapping of the partial products. A configurable hardware correction strategy is also proposed to enable graceful quality degradation, as well as more aggressive energy reduction at a given quality. When applied to Wallace multipliers, the proposed approach achieves quality, in terms of Mean Error Distance, up to 11× higher than the conventional dynamic truncation, at the same energy. In the case study of Discrete Cosine Transform compression, the proposed approximate multiplier reaches image qualities by 15-35% better, compared to prior art.