In-memory analog matrix computing (AMC) with resistive random-access memory (RRAM) represents a highly promising solution that solves matrix problems in one step. However, the existing AMC circuits each have a specific connection topology to implement a single computing function, lack of the universality as a matrix processor. In this work, we design a reconfigurable AMC macro for general-purpose matrix computations, which is achieved by configuring proper connections between memory array and amplifier circuits. Based on this macro, we develop a hybrid system that incorporates an on-chip write-verify scheme and digital functional modules, to deliver a general-purpose AMC solver for various applications.
Physical Analogue Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units
NanoElectronics Group, MESA+ Institute and BRAINS Center for Brain-Inspired Computing, University of Twente（纳米电子组、MESA+研究所和脑启发计算中心、代尔夫特理工大学）；CAES Group and BRAINS Center for Brain-Inspired Computing, University of Twente（CAES组和脑启发计算中心、代尔夫特理工大学）
RACE-IT: A Reconfigurable Analog Computing Engine for In-Memory Transformer Acceleration
Artificial Intelligence Research Lab (AIRL), Hewlett Packard Labs, USA（人工智能研究实验室（AIRL）、惠普实验室，美国）；Technion - Israel Institute of Technology, Haifa, Israel（技术ion-以色列理工学院，海法，以色列）
You Only Charge Once 2.0 : A End-to-End Analog Computing-in-Memory Architecture with Reconfigurable Switched Capacitors
The Hong Kong University of Science and Technology（香港科技大学）；AI Chip Center for Emerging Smart Systems (ACCESS), InnoHK, Hong Kong（香港创新署新兴智能系统芯片中心）；Huazhong University of Science and Technology（华中科技大学）；Shenzhen University of Advanced Technology（深圳先进技术研究院）
