Leveraging Highly Approximated Multipliers in DNN Inference

Georgios Zervakis, Fabio Frustaci, Ourania Spantidi +3

In this work, we present a control variate approximation technique that enables the exploitation of highly approximate multipliers in Deep Neural Network (DNN) accelerators. Our ap…