Single pulse blind-write with gradient accumulation strategy tolerant to non-idealities of memristive synapse for in-memory learning

作者：Linkun Wang, Yang Li, Hongming Mou, Ziang Chen, Changjian Li, Wei Wang · 发表于：Journal of Physics D Applied Physics · 年份：2025 · DOI：10.1088/1361-6463/add0c2 · 被引用次数：3 · 研究领域：Advanced Memory and Neural Computing、Neural dynamics and brain function、Photoreceptor and optogenetics research

Abstract Memristor-based analog in-memory learning (AIML) has emerged as a promising approach to improve energy efficiency in deep neural network training. However, non-idealities in memristive devices, such as nonlinearity, asymmetry, and cycle-to-cycle (C2C) and device-to-device (D2D) variations, pose significant challenges. These issues lead to increased energy consumption, reduced write precision, and compromised in-situ learning performance. To address these problems, we propose a mixed-precision training strategy that combines gradient accumulation with single pulse blind write method. We analyze the failure mechanisms of in-situ learning without these techniques and systematically investigate how various non-idealities affect AIML performance. We demonstrate that, by using our GA-Single Pulse strategy, high accuracy (95.36%) can be achieved even under significant non-idealities, including device conductance states being limited to 10 pulses for potentiation as well as 5 pulses for depression, the asymmetry of conductance state constrained to a factor of 2, the nonlinearity in long-term potentiation/ long-term depression curve reaching up to 5, C2C variation as high as 50%, and D2D variation extending up to 40% for learning handwritten digits in MNIST handwritten digit dataset, outperforms all previous reports. The results suggest that the idealities of memristive devices may not be as critical as previously assumed for AIML’s practical deployment.