A Scalable and Configurable Low-Power Mixed Signal Neuromorphic Accelerators for Spiking Neural Network

Spiking Neural Network (SNN) mimics the biologically inspired neural network that benefits for the computing with low power and high parallelism. In this paper, a scalable and configurable low-power mixed signal neuromorphic accelerator is proposed for the SNN application. It consists of 64 neurons, 64 synapses, 64×64 Static Random-Access Memory (SRAM) array, 64×64 Content Addressable Memory (CAM) array and a number of digital asynchronous routers. 15×64 neurons are fully connected by proposed scalable architecture. The proposed neuromorphic accelerator is designed in 65 nm CMOS process dissipating 4.8 mW at 100 MHz of clock frequency from a 1.2 V power supply.