Neural network-based accelerators for transcendental function approximation

Abstract

The general-purpose approximate nature of neural network (NN) based accelerators has the potential to sustain the historic energy and performance improvements of computing systems. We propose the use of NN-based accelerators to approximate mathematical functions in the GNU C Library (glibc) that commonly occur in application benchmarks. Using our NN-based approach to approximate cos, exp, log, pow, and sin we achieve an average energy-delay product (EDP) that is 68x lower than that of traditional glibc execution. In applications, our NN-based approach has an EDP 78% of that of traditional execution at the cost of an average mean squared error (MSE) of 1.56.