eBrainISA: Edge-Oriented Instruction Set Architecture for Hybrid Brain-Inspired Computing

Hybrid brain-inspired computing, which integrates computer science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs), represents a significant trend in the field of brain-inspired computing. This approach demonstrates considerable advantages in domains such as perception, cognition, and learning. Nevertheless, several challenges persist in deploying hybrid brain-inspired computing on resource-constrained edge devices, particularly in enabling these devices to effectively manage complex tasks. To tackle these challenges, we present eBrainISA, an edge-oriented hybrid brain-inspired computing instruction set architecture. eBrainISA is designed to support sophisticated ANNs, SNNs, and hybrid neural networks (HNNs), which integrates an extensive suite of neural operators that are tailored to accommodate the hardware limitations on edge devices. To validate the proposed architecture, we develop a unified low-precision quantization approach alongside a hardware simulator. Prior instruction set architectures offer limited support for hybrid brain-inspired computing, while ours can support all major networks including ANNs such as ResNet and Yolo, SNNs such as Spike_ResNet and Spike_Transformer, and HNNs such as DetectHNN and ReSpike with <1% accuracy degradation. Moreover, our architecture achieves about 5× memory saving compared to full-precision versions and 3.87-53.61× higher energy efficiency compared to edge GPU when executing different neural networks with diverse behaviors. This work demonstrates a promising balance between intelligent demands for general models and resource constraints on edge devices.