EERA-DNN: An energy-efficient reconfigurable architecture for DNNs with hybrid bit-width and logarithmic multiplier

Abstract

This paper proposes an energy-efficient reconfigurable architecture for deep neural networks (EERA-DNN) with hybrid bit-width and logarithmic multiplier. To speed up the computing and achieve high energy efficiency, we first propose an efficient network compression method with hybrid bit-width weights scheme, that saves the memory storage of network LeNet, AlexNet and EESEN by 7x–8x with negligible accuracy loss. Then, we propose an approximate unfolded logarithmic multiplier to process the multiplication operations efficiently. Comparing with state-of-the-art architectures EIE and Thinker, this work achieves over 1.8x and 2.7x better in energy efficiency respectively.