The Perfect Match: Selecting Approximate Multipliers for Energy-Efficient Neural Network Inference

Reconfigurable approximate multipliers have been proposed as a way to improve the energy efficiency of neural network inference. However, selecting the optimal combination of approximate modes is a challenging problem due to the tradeoff between energy savings and accuracy loss. In this paper, we propose a methodology for selecting the best triad of approximate multipliers to form a reconfigurable approximate multiplier that can satisfy a maximum accuracy drop threshold and achieve the highest possible energy savings. We use formal methods to produce a Pareto-front of solutions that satisfy the accuracy constraint and maximize energy savings. Experimental results show that our methodology can achieve significant gains in energy with negligible drops in accuracy when compared to the baseline.