Spiking Neural Networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture(ISA), which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind in neuron scale. The experimental results showed that code density was improved up to 28.3x in Darwin3, and neuron core fan-in and fan-out were improved up to 4096x and 3072x by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8X and 200.8X when mapping convolutional spiking neural networks (CSNN) onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.

翻译：脉冲神经网络（SNNs）因其生物 plausibility 及潜在的计算效率提升而日益受到关注。为匹配SNNs中高时空动态特性，亟需采用基于硬件神经元和突触电路直接执行SNNs的神经形态芯片。本文提出一种名为Darwin3的大规模神经形态芯片，其配备新型指令集架构（ISA），包含10条主要指令及若干扩展指令。该架构支持灵活的神经元模型编程与局部学习规则设计。Darwin3芯片采用计算节点网格设计与创新路由算法，并通过压缩机制表示突触连接，显著降低内存占用。该芯片支持高达235万个神经元，是同类芯片中规模最大的。实验结果表明，Darwin3的代码密度提升达28.3倍；通过连接压缩技术，神经元核心扇入与扇出较物理存储深度分别提升4096倍与3072倍。将卷积脉冲神经网络（CSNN）映射至该芯片时，内存节省达6.8倍至200.8倍，在精度与延迟方面展现出相较于其他神经形态芯片的最优性能。