An Energy Efficient Residual Spiking Neural Network Accelerator With Ternary Spikes

Spiking neural networks (SNNs) use discrete binary spikes to transfer information between neurons, which is different from artificial neural networks (ANNs). Although event-based characteristics bring potential computation power and efficiency to SNNs, the long processing time window of discrete spikes leads to high latency. In this brief, a spike version of the residual network using ternary spikes is proposed. A shorter time window is required to achieve competitive performance because the ability to transfer information of the ternary spikes is strengthened. An SNN accelerator based on the proposed residual network with ternary spikes is designed and implemented with 28 nm CMOS technology, and the core area is 0.63 mm. The proposed SNN accelerator achieves the classification accuracy of 92.07% on CIFAR-10 dataset with SResNet20 and only 6 time steps. The accelerator achieves 0.39 mJ energy consumption per frame with a throughput of 165.7 FPS when running at 500 MHz.