An Energy-Efficient Neuromorphic Processor Using Unified Refractory Control-Based NoC for Edge AI

Neuromorphic computing has emerged as a promising paradigm for edge AI systems owing to its event-driven operation and high energy efficiency. However, conventional spiking neural network (SNN) architectures often suffer from redundant computation and inefficient power control, particularly during on-chip learning. This paper proposes a network-on-chip (NoC) architecture featuring a unified refractory-enabled neuron (UREN)-based router that globally coordinates spike-driven computation across multiple neuron cores. The router applies a unified refractory time to all neurons following a winner spike event, effectively enabling clock gating and suppressing redundant activity. The proposed design adopts a star-routing topology with multicasting support and integrates nearest-neighbor spike-timing-dependent plasticity (STDP) for local online learning. FPGA-based experiments demonstrate a 30% reduction in computation and 86.1% online classification accuracy on the MNIST dataset compared with baseline SNN implementations. These results confirm that the UREN-based router provides a scalable and power-efficient neuromorphic processor architecture, well suited for energy-constrained edge AI applications.