Update Disturbance-Resilient Analog ReRAM Crossbar Arrays for In-Memory Deep Learning Accelerators

2026-08-26

AI Summary

研究针对ReRAM交叉阵列在并行权重更新时的干扰问题，提出了一种基于铪氧化物层中导电细丝的ReRAM设备解决方案，实现了快速、非易失性切换和抗干扰能力。

Abstract

Resistive memory (ReRAM) technologies with crossbar array architectures hold significant potential for analog AI accelerator hardware, enabling both in-memory inference and training. Recent developments have successfully demonstrated inference acceleration by offloading compute-heavy training workloads to off-chip digital processors. However, in-memory acceleration of training algorithms is crucial for more sustainable and power-efficient AI, but still in an early stage of research. This study addresses in-memory training acceleration using analog ReRAM arrays, focusing on a key challenge during fully parallel weight updates: disturbances of the weight values in cross-point devices. A ReRAM device solution is presented on 350 nm silicon technology, utilizing a resistive switching conductive metal oxide (CMO) formed on a nanoscale conductive filament within a HfOx layer. The devices not only exhibit 60 ns fast, non-volatile analog switching, but also demonstrates outstanding resilience to update disturbances, enduring over 100k pulses. The disturbance tolerance of the ReRAM is analyzed using COMSOL Multiphysics simulations, modeling the filament-induced thermoelectric energy concentration that results in a highly nonlinear device responses to input voltage amplitudes. Disturbance-free parallel weight mapping is also demonstrated on the back-end-of-line integrated ReRAM array chip. Finally, comprehensive hardware-aware neural network simulations validate the potential of our ReRAM for in-memory deep learning accelerators capable of fully parallel weight updates.

Problem

• in-memory training
• weight updates
• disturbances
• ReRAM crossbar arrays
• analog AI accelerator

Innovation

• analog ReRAM
• update disturbances resilience
• parallel weight updates
• thermoelectric energy concentration
• hardware-aware neural network simulations