The game-changing memristive technology for next-gen AI/ML hardware

Abstract

Neuromorphic computing is of high importance in Artificial Intelligence (AI) and Machine Learning (ML) to sidestep challenges inherent to neural-inspired computations in modern computing systems. Throughout the development history of neuromorphic computing, Compute-In-Memory (CIM) with emerging memory technologies, such as Resistive Random-Access Memory (RRAM), offer advantages by performing tasks in place, in the memory itself, leading to significant improvement in architectural complexity, data throughput, area density, and energy efficiency. In this article, in-house research efforts in designing and applying innovative memristive circuitry for AI/ML related workloads are showcased. To be specific, Multiply-and-Accumulate (MAC) operations and classification tasks can be obtained on a crossbar array made of 1-transistor-1-RRAM (1T1R) cells. With the same circuit structure, flow-based Boolean arithmetic is made possible by directing the paths of current flow through the crossbar. Better yet, high-precision operations for in-situ training can be realized with an enhanced crossbar array made of 6-transistor-1-RRAM (6T1R) cells alongside the bidirectional current control mechanism. Where possible, our neuromorphic solutions optimized for AI-enabled cognitive operations offer faster and more robust yet more efficient decision-making to support future battlespaces.