MICSim: A Modular Simulator for Mixed-signal Compute-in-Memory based AI Accelerator

TL;DR

MICSim is an open-source, modular simulator for mixed-signal compute-in-memory AI accelerators, enabling flexible early-stage evaluation of hardware/software performance with significant speed improvements.

Contribution

It introduces MICSim, a highly configurable, extendable simulation framework for CIM accelerators supporting diverse designs and network models, with integrated software-hardware evaluation capabilities.

Findings

Achieves 9x-32x speedup over NeuroSim.

Supports CNNs and Transformers in Python.

Enables efficient design space exploration.

Abstract

This work introduces MICSim, an open-source, pre-circuit simulator designed for early-stage evaluation of chip-level software performance and hardware overhead of mixed-signal compute-in-memory (CIM) accelerators. MICSim features a modular design, allowing easy multi-level co-design and design space exploration. Modularized from the state-of-the-art CIM simulator NeuroSim, MICSim provides a highly configurable simulation framework supporting multiple quantization algorithms, diverse circuit/architecture designs, and different memory devices. This modular approach also allows MICSim to be effectively extended to accommodate new designs. MICSim natively supports evaluating accelerators' software and hardware performance for CNNs and Transformers in Python, leveraging the popular PyTorch and HuggingFace Transformers frameworks. These capabilities make MICSim highly adaptive when simulating different networks and user-friendly. This work demonstrates that MICSim can easily be combined with optimization strategies to perform design space exploration and used for chip-level Transformers CIM accelerators evaluation. Also, MICSim can achieve a 9x - 32x speedup of NeuroSim through a statistic-based average mode proposed by this work.