Fully memristive spiking neural network for energy-efficient graph learning
Tuo Shi, Lili Gao, Ruixi Zhou, Yang Tian, Pei Chen, Yanting Ding, Shuangzhu Tang, Huiqin Ma, Jian Lu, Hui Zhang, Z. Wang, Bo Lyu, Xumeng Zhang, Xiaobing Yan, Qi Liu
Abstract
Parallel and energy-efficient searching of the shortest paths on a large graph is challenging. Conventional methods commonly used are sequential and computing intensive, rendering them inadequate for addressing large-scale and real-time situations. Here, we propose a highly parallel, computation- and energy-efficient approach to shortest path-based graph learning based on an emerging memristor spiking neural network via algorithm-device codesign. The shortest path is obtained parallelly in nature using simultaneous spike traveling instead of arithmetic calculation, achieving extremely low time and space complexity. A nonlinear weight mapping approach is proposed to counterbalance the neuron intrinsic nonlinearity to guarantee accuracy to support large-scale graphs. The memristor hardware capability is experimentally demonstrated in unsupervised and supervised classification tasks. The estimated energy efficiency of 517.82 giga-traversal edges per second per watt outperforms field programmable gate arrays by three to four orders of magnitude, providing a pathway toward highly energy-efficient graph computing hardware.