X-Sim: An Accurate and Scalable Simulator for Memristive Computing-in-Memory Accelerators

Le résumé fourni par la source

Computing-in-Memory (CIM) architectures using memristive crossbar arrays enable energy-efficient AI acceleration. Analog non-idealities, such as IR drop and nonlinearity, impose design constraints that existing simulators cannot capture and thus explore effectively. Current approaches sacrifice either modeling accuracy or simulation speed, preventing systematic design space exploration. In this paper we propose X-Sim, a crossbar simulator that resolves this trade-off through a modular architecture. Our approach decouples device physics from circuit analysis using a fixed-point scheme, avoiding expensive Jacobian computations while preserving device fidelity. X-Sim delivers SPICE-level accuracy (< 1% error) with up to 200× speedup over physics-based simulators. This enables quick and systematic design space exploration across thousands of configurations, guiding reliable system design. X-Sim will be released as open source.

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