A power-aware digital feedforward neural network platform with backpropagation driven approximate synapses
Jaeha Kung, Duck-Hwan Kim, Saibal Mukhopadhyay · 2015
This paper proposes a power-aware digital feedforward neural network platform that utilizes the backpropagation algorithm during training to enable energy-quality trade-off. Given a quality constraint, the proposed approach identifies a set of synaptic weights for approximation in a neural network. The approach selects synapses with small impact on output error, estimated by the backpropagation algorithm, for approximation. The approximations are achieved by a coupled software (reduced bit-width) and hardware (approximate multiplication in the processing engine) based design approaches. The full-chip design in 130nm CMOS shows, compared to a baseline accurate design, the proposed approach reduces system power by ~38% with 0.4% lower recognition accuracy in a classification problem.