Journal Article

Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip

Demirci, Tugba, Wu, Chenxi, Richter, Ole, Fang, Wei, Xing, Yannan, Yan, Tianyi, Nielsen, Carsten, Wang, Dingheng, De Marchi, Michele, Xu, Bo, Li, Guoqi, Hu, Tianxiang, Deng, Lei, Yao, Man, Tian, Yonghong, Sheik, Sadique, Zhao, Guangshe, Qiao, Ning

2024

Overview

By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a promising energy-efficient machine intelligence. How to borrow high-level brain dynamic mechanisms to help neuromorphic computing achieve energy advantages is a fundamental issue. This work presents an application-oriented algorithm-software-hardware co-designed neuromorphic system for this issue. First, we design and fabricate an asynchronous chip called “Speck”, a sensing-computing neuromorphic system on chip. With the low processor resting power of 0.42mW, Speck can satisfy the hardware requirements of dynamic computing: no-input consumes no energy. Second, we uncover the “dynamic imbalance” in spiking neural networks and develop an attention-based framework for achieving the algorithmic requirements of dynamic computing: varied inputs consume energy with large variance. Together, we demonstrate a neuromorphic system with real-time power as low as 0.70mW. This work exhibits the promising potentials of neuromorphic computing with its asynchronous event-driven, sparse, and dynamic nature. Mimicking high-level abstraction of the brain to achieve energy advantages is a fundamental issue in neuromorphic computing. Here, the authors fabricate an asynchronous chip and demonstrate a high-accuracy neuromorphic system with power consumption of 0.7mW.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/705/1042 / 639/705/117 / Action Potentials - physiology / Algorithms / Brain / Brain - physiology / Energy efficiency / Firing pattern / Hardware / Humanities and Social Sciences / Humans / Microprocessors / Mimicry / Models, Neurological / multidisciplinary / Neural networks / Neural Networks, Computer / Neuromorphic computing / Neurons - physiology / Power consumption / Power management / Science / Science (multidisciplinary) / Software / Synapses / Synapses - physiology / System on chip