Energy- and Area-efficient Fe-FinFET-based Time-Domain Mixed-Signal Computing In Memory for Edge Machine Learning
Jin Luo, Weikai Xu, Yide Du, Boyi Fu, Jiahao Song, Zhiyuan Fu, Mengxuan Yang, Yiqing Li, Le Ye, Qianqian Huang, Ru Huang
Abstract
In this work, for the first time, ferroelectric (FE)-FET is proposed to implement time-domain (TD) computing in memory with nonvolatility (nvCIM) for both multiply-accumulate (MAC) operation and activation function with the record highest area and energy efficiency. Benefiting from the three-terminal transistor structure, the fabricated FeFET based on 14nm-node FinFET can function as both a nonvolatile element for weight storage and a controllable switch for neural network inputs modulation simultaneously, thereby realizing local multiplication of MAC in the proposed FE delay unit with only 2T-1FeFET. The novel dual-edge operation is also demonstrated for TD MAC, enabling further energy efficiency improvement. Furthermore, by utilizing the physics of time-dependent gradual polarization switching, FeFET as the activation function for TD nvCIM with additional memory trace behavior is experimentally demonstrated with only single transistor. Based on the proposed FE-based TD nvCIM design, high-accuracy pattern recognition and accelerated deep reinforcement learning are also demonstrated with scaled voltage of 0.5V, providing a promising area- and energy-efficient mixed-signal neural network for edge AI.