A Multi-bit ECRAM-Based Analog Neuromorphic System with High-Precision Current Readout Achieving 97.3% Inference Accuracy.

Minseong Um, Minil Kang, Kyeongho Eom, Hyunjeong Kwak, Kyungmi Noh, Jimin Lee, Jeonghoon Son, Jiseok Kwon, Seyoung Kim, Hyung-Min Lee
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Abstract

This article proposes an analog neuromorphic system that enhances symmetry, linearity, and endurance by using a high-precision current readout circuit for multi-bit nonvolatile electro-chemical random-access memory (ECRAM). For on-chip training and inference, the system uses activation modules and matrix processing units to manage analog update/read paths and perform precise output sensing with feedback-based current scaling on the ECRAM array. The 250nm CMOS neuromorphic chip was tested with a 32 x 32 ECRAM synaptic array, achieving linear and symmetric updates and accurate read operations. The proposed circuit system updates the 32 x 32 ECRAM across 100 levels, maintaining consistent synaptic weights, and operates with an output error rate of up to 2.59% per column. It consumes 5.9 mW of power excluding the ECRAM array and achieves 97.3% inference accuracy on the MNIST dataset, close to the software-confirmed 97.78%, with only the final layer (64 x 10) mapped to the ECRAM.

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基于多位 ECRAM 的模拟神经形态系统,具有高精度电流读取功能,推理精确度达 97.3%。
本文提出了一种模拟神经形态系统,通过使用多位非易失性电化学随机存取存储器(ECRAM)的高精度电流读出电路来增强对称性、线性度和耐用性。在片上训练和推理方面,该系统使用激活模块和矩阵处理单元来管理模拟更新/读取路径,并通过 ECRAM 阵列上基于反馈的电流缩放来执行精确的输出感应。250nm CMOS 神经形态芯片通过 32 x 32 ECRAM 突触阵列进行了测试,实现了线性对称更新和精确读取操作。所提出的电路系统对 32 x 32 ECRAM 进行了 100 级更新,保持了一致的突触权重,每列输出误差率高达 2.59%。它的功耗为 5.9 mW(不包括 ECRAM 阵列),在 MNIST 数据集上实现了 97.3% 的推理准确率,接近软件确认的 97.78%,其中只有最后一层(64 x 10)映射到了 ECRAM。
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