An FPGA-Based Event-Driven SNN Accelerator for DVS Applications With Structured Sparsity and Early-Stop

This paper proposed an algorithm-hardware co-design of an event-driven spiking neural network (SNN) accelerator for classification tasks of event-based data from dynamic vision sensors (DVS), which can implement a feed-forward SNN with a maximum network size of 1 million synapses. Configurable structured sparsity is introduced between the first layer and the second layer to improve energy efficiency and balance the workload between different processing elements (PEs). The number of available neurons in the accelerator is sparsity-dependent and ranges from 1024 to 4096. The modified leaky-integrate-fire (LIF) neuron model and an event-driven neuron update scheme are employed in both algorithm and hardware to fully utilize the natural sparsity of DVS event stream. Early-stop inference strategy on the hardware enables a trade-off between inference accuracy and efficiency. A three-layer fully connected SNN is trained through backpropagation through time (BPTT) and is implemented and evaluated on Xilinx ZCU104 FPGA. Our design can achieve 96.0% accuracy on the N-MNIST dataset and 79.0% accuracy on the DVS128-Gesture dataset both at 50% sparsity. The top performance of the accelerator on ZCU104 is 3.22 GSOP/S and 3.99 GSOP/W at 250 MHz.