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.
From: Junbin Long [view email]
[v1]
Sat, 13 Jun 2026 14:31:34 UTC (2,061 KB)
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