An Asynchronous Reconfigurable SNN Accelerator With Event-Driven Time Step Update

In this paper, we put forward an asynchronous spiking neural network (SNN) accelerator with 1024 neurons and 1 million synapses, which is reconfigurable in terms of network connection and neuron parameters. Bundled data asynchronous circuits are adopted to design the neuromorphic computation core and mesh network. Multicast communication is used to transmit packet among and within each core for less packet transmission and better energy efficiency. A novel time step update mechanism, which updates neurons in an event-driven manner without considering the chip-wide activity of other unrelated neurons, is proposed to improve the performance of speed. The SNN accelerator is verified by classifying MNIST handwritten digit with Xilinx VC707 FPGA. The results show that the accelerator achieves 98% accuracy with MINST database, and more than 1 GIPS/W energy efficiency which is 32 times better than previous work.