An Edge Neuromorphic Processor With High-Accuracy On-Chip Aggregate-Label Learning

Neuromorphic computing with bio-inspired spiking neural networks (SNNs) offers an energy-efficient paradigm for edge intelligence. But, achieving practically high on-chip SNN learning accuracy with limited hardware resources still remains challenging. To tackle this issue, this brief proposes an edge neuromorphic processor architecture enabling computationally-simple optimized aggregate-label (AL) algorithm to realize high-accuracy on-chip learning. To maximize utilization of multicore resources and minimize processing latency, our processor adopts techniques including uniform neuron-core mapping, layer-alternating workflow, time-step pipeline and event-driven scheme. We benchmarked our processor on an FPGA device, and attained high on-chip learning accuracies of 97.21%, 88.1%, 90.92%, 100% and 99.22% on MNIST, Fashion-MNIST, ETH-80, ORL-10, and Yale-10 datasets, respectively, using a small 2-layer fully-connected (FC) SNN, with a moderate resource cost and a relatively high energy efficiency. These results indicate that our design is very useful for many self-adaptive edge systems.