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

2026-06-13

AI Summary

This work addresses the significant accuracy degradation in closed-loop analog matrix computation (AMC) circuits caused by non-idealities such as device programming errors, thermal noise, operational amplifier offset, and interconnect resistance, which existing simulation methods struggle to model accurately and efficiently. To overcome these limitations, the paper introduces SimAMC, the first simulator capable of efficiently and accurately simulating closed-loop AMC circuits incorporating a comprehensive set of non-ideal effects. SimAMC employs an alternating iterative algorithm to precisely model real-valued matrix operations and integrates detailed non-ideality models. Experimental results demonstrate that SimAMC achieves excellent agreement with SPICE-level simulations while offering speedups of several orders of magnitude, thereby substantially accelerating the design and evaluation of AMC circuits.

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.

Problem

• analog matrix computing
• resistive memory
• hardware non-idealities
• closed-loop circuits
• circuit simulation

Innovation

• analog matrix computing
• resistive memory
• non-idealities modeling
• closed-loop circuits
• fast simulation