An exponential function accelerator with radix-16 algorithm for spiking neural networks

A range reduction method for shift-and-add algorithms for ex-ponential functions is proposed in this paper. An exponential function ac-celerator with this method and radix-16 shift-and-add algorithm has been implemented in SMIC 55 nm CMOS process. Compared with the ex-isting method, the proposed method reduces the latency (cycles) by 33% and 20% for 16 and 32-bit precision results, respectively; thereby increas-ing the throughput to 50M exp/s and reducing the energy consumption to 4.6 pJ/exp. In addition, this method saves die area since no arithmetic units are adopted. This exponential accelerator is supposed to be used in a neuromorphic chip for spiking neural network modeling.