Energy-Efficient Approximate Multiplication for Digital Signal Processing and Classification Applications

Abstract

The need to support various digital signal processing (DSP) and classification applications on energy-constrained devices has steadily grown. Such applications often extensively perform matrix multiplications using fixed-point arithmetic while exhibiting tolerance for some computational errors. Hence, improving the energy efficiency of multiplications is critical. In this brief, we propose multiplier architectures that can tradeoff computational accuracy with energy consumption at design time. Compared with a precise multiplier, the proposed multiplier can consume 58% less energy/op with average computational error of similar to 1%. Finally, we demonstrate that such a small computational error does not notably impact the quality of DSP and the accuracy of classification applications.