Shichun Sun

Zhejiang Lab

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Fangchao Yang

Nanjing University of Science and Technology

Huajin Tang

Zhejiang University

Xiaofei Jin

Zhejiang University

Peng Lin

Zhejiang University of Science and Technology

Xiaolei Zhu

Zhejiang Lab

Xundong Wu

Zhejiang Lab

Yitao Li

Zhejiang University of Science and Technology

Gang Pan

Zhejiang Lab

De Ma

Zhejiang University

Youneng Hu

Zhejiang University of Science and Technology

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Zhejiang Lab

Zhejiang University

Zhejiang University of Science and Technology

Chinese Academy of Sciences

Zhejiang Province Institute of Architectural Design and Research

Hangzhou Dianzi University

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Nanjing University of Science and Technology

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Micro & Nano Research Institute

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Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning

National Science Review (2024)

De Ma Xiaofei Jin Shichun Sun Yitao Li Xundong Wu

ABSTRACT 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, 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 on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.

Neuromorphic engineering

10.1093/nsr/nwae102

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