Low-Cost Adaptive Exponential Integrate-and-Fire Neuron Using Stochastic Computing
Shanlin Xiao, Wei Liu, Yuhao Guo, Zhiyi Yu
Abstract
Neurons are the primary building block of the nervous system. Exploring the mysteries of the brain in science or building a novel brain-inspired hardware substrate in engineering are inseparable from constructing an efficient biological neuron. Balancing the functional capability and the implementation cost of a neuron is a grand challenge in neuromorphic field. In this paper, we present a low-cost adaptive exponential integrate-and-fire neuron, called SC-AdEx, for large-scale neuromorphic systems using stochastic computing. In the proposed model, arithmetic operations are performed on stochastic bit-streams with small and low-power circuitry. To evaluate the proposed neuron, we perform biological behavior analysis, including various firing patterns. Furthermore, the model is synthesized and implemented physically on FPGA as a proof of concept. Experimental results show that our model can precisely reproduce wide range biological behaviors as the original model, with higher computational performance and lower hardware cost against state-of-the-art AdEx hardware neurons.