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arXiv · 2606.15322

SimAMC: A Fast and Accurate Simulator for Resistive Memory-Based Analog Matrix Computing with Non-Idealities

Junbin Long·Mu Zhou·Saitao Zhang·Yongxiang Li·Zhong Sun
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Abstract

Analog matrix computing (AMC) circuits leverage resistive memory arrays to perform matrix operations in a massively parallel manner, providing an efficient approach for accelerating data-intensive tasks. However, hardware non-idealities severely impact computational accuracy, making early-stage simulation vital for reliable performance estimation and design optimization. While open-loop circuits for matrix-vector multiplication are well-studied, closed-loop AMC circuits, which solve matrix equations, are computationally more complex and substantially more sensitive to non-idealities, complicating their simulation. In this work, we present SimAMC, a simulator for resistive memory-based closed-loop AMC circuits. SimAMC is capable of modeling matrix inversion and eigenvector solving in the presence of key non-idealities, including device programming error, data conversion error, thermal noise, operational amplifier input offset, and interconnect resistance. For real-valued matrix computing circuits, an alternating iterative algorithm is designed. SimAMC's effectiveness is validated through comparison with SPICE, showing excellent agreement while also demonstrating a speedup of several orders of magnitude.

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Junbin Long, Mu Zhou, Saitao Zhang, Yongxiang Li, Zhong Sun. 2026-06-13. SimAMC: A Fast and Accurate Simulator for Resistive Memory-Based Analog Matrix Computing with Non-Idealities. https://arxiv.org/abs/2606.15322

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