FEAS: A Faster Event-driven Accelerator Supporting Inhibitory Spiking Neural Network

Abstract

In recent years, the spiking neural network (SNN) has gained widespread attention for its low latency and low power consumption characteristics. Customizing accelerators to exploit the parallelism in SNN fully has become a current research hotspot. Because the calculations between different time steps in SNN have dependencies, the existing accelerators update the neuron state strictly in time sequence, which results in the calculation of SNN even more than that of the artificial neural network (ANN). In addition, traditional event-driven SNN accelerators cannot support inhibitory SNN models, which severely limits their scope of application. Based on this observation, we combined the characteristics of the most commonly used neuron model and spike code to design an event-driven SNN accelerator (FEAS). FEAS realizes the acceleration for SNN (both the excitatory SNN and the inhibitory SNN) models through algorithm approximation and novel hardware modules. We use FEAS to accelerate a fully connected SNN model for classification on the MNIST dataset. The results show that compared with the state-of-the-art technology, our accelerator can increase the throughput by an order of magnitude with the model's accuracy as high as 97.54%.