MICSim可快速模拟存内计算芯片性能，助力AI加速器设计优化。
MICSim: A Modular Simulator for Mixed-signal Compute-in-Memory based AI Accelerator
- 模块化架构支持多层级协同设计与灵活配置
- 对CNN和Transformer实现9~32倍于NeuroSim的仿真速度提升
- 兼容PyTorch/HuggingFace，适合芯片级AI加速器研发者使用
本文提出MICSim，一个开源的预电路级模拟器，用于早期评估混合信号存内计算（CIM）加速器的芯片级软件性能与硬件开销。该工具基于先进模拟器NeuroSim模块化构建，支持多种量化算法、电路/架构设计及存储器件，具备高度可配置性。其模块化设计便于扩展新架构。MICSim原生支持在Python中利用PyTorch和HuggingFace Transformers框架评估CNN与Transformer的软硬件性能，适应性强且易用。实验表明，通过本工作提出的统计平均模式，MICSim相较NeuroSim实现了9至32倍的速度提升，并可结合优化策略进行设计空间探索，适用于芯片级Transformer CIM加速器的评估。
原文摘要 · Abstract (English)
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.
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