Modular building blocks for mapping spiking neural networks onto a programmable neuromorphic processor. (November 2022)

• Record Type: Journal Article
• Authors: Zou, Chenglong; Cui, Xiaoxin; Chen, Guang; Feng, Shuo; Liu, Kefei; Wang, Xinan; Wang, Yuan
• Is Part Of: Microelectronics journal. Volume 129(2022)
• Publication Date: 2022-11
• Subjects: Artificial neural networks (ANNs) -- Spiking neural networks (SNNs) -- Neuromorphic processor -- Mapping methodology -- Image classification; Microelectronics -- Periodicals; Microélectronique -- Périodiques; Microelectronics; Electronic journals; Journals - contents and abstracts; Periodicals; 621.3805
• DOI: 10.1016/j.mejo.2022.105612
• Languages: English
• ISSNs: 0959-8324

Abstract

Abstract: In the past few years, brain-inspired artificial intelligence (AI) system with energy-efficient neuromorphic computing has gathered lots of interests. However, how to build an efficient algorithm-to-hardware development tool for spiking neural networks (SNNs) remains a big challenge. In this paper, we present a novel design of neural controller which can flexibly generate the store-and-release signals in the designed SNNs. Further, we propose a mapping methodology with modular building blocks to deploy SNN models onto a many-core programmable neuromorphic processor. Experimental results show that the presented mapping method can be adaptive to various SNN layers of different sizes and quantization precisions. Besides, our demonstrated system on chip can achieve about 181 and 26 images per second runtime inference speed on MNIST and CIFAR-10 dataset respectively and show comparable accuracies and significantly better power performance with their artificial neural network (ANN) counterparts running on traditional GPU.