A 0.66-mm² 0.49 pJ/SOP SNN Processor With Temporal-Spatial Post-Neuron-Processing and Model-Adaptive Crossbar in...

This paper presents a Spiking Neural Network (SNN) processor specifically designed to overcome the limitations of existing parallel architectures in maintaining high energy efficiency and model adaptability in a compact area footprint for Artificial Intelligence of Things (AIoT). This is achieved through two key design features: a Temporal-Spatial Post-Neuron Processing (PoNP) scheme that efficiently reuses membrane potential, maximizes parallelism, and reduces memory bank requirements; and a Model-Adaptive Crossbar design with pre-configured parameters and a dynamic switching mechanism enables processing of various SNN models through operation orchestration without efficiency degradation. Using an 8-way parallel pipeline design, the processor achieves a throughput of 128 Synaptic Operations (SOPs) per cycle, resulting in a 2.8$\boldsymbol{\times}$ enhancement in energy efficiency. Fabricated in a 40-nm CMOS process, the chip occupies a compact core area of 0.66 mm${}^{2}$. It achieves a power consumption of 6.26 mW, an energy efficiency of 0.49 pJ/SOP, and a throughput of 12.8 GSOPS at 0.75 V, 100 MHz. The chip is evaluated using typical spatial, temporal, and temporal-spatial datasets, including MIT-BIH, MNIST, N-MNIST, NavGesture, and SHD. These results demonstrate that our chip achieves best-in-class in terms of energy efficiency and latency compared to state-of-the-art architectures.