SpinLiM: Spin Orbit Torque Memory for Ternary Neural Networks Based on the Logic-in-Memory Architecture
Lichuan Luo, He Zhang, Jinyu Bai, Youguang Zhang, Wang Kanga and Weisheng Zhao
School of Integrated Circuit Science and Engineering, Beihang University, Beijing, 100191, China
awang.kang@buaa.edu.cn
ABSTRACT
Logic-in-memory architecture based on spintronic memories shows fascinating prospects in neural networks (NNs) for its high energy efficiency and good endurance. In this work, we leveraged two magnetic tunnel junctions (MTJs), which are driven by the interplay of field-free spin orbit torque (SOT) and spin transfer torque (STT) effects, to achieve a novel stateful logic-inmemory paradigm for ternary multiplication operations. Based on this paradigm, we further proposed a highly parallel array structure to serve for ternary neural networks (TNNs). Our results demonstrate the advantage of our design in power consumption compared with CPU, GPU and other state-of-the-art works.
Keywords: Stateful Logic-In-Memory, Magnetic Tunnel Junction, Ternary Neural Network, Spin Orbit Torque Memory.
