mlsanityData to 14 Oct 2026

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

arXiv 2409.14838 ↗First posted 23 Sep 2024cs.AI cs.ARcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 34 references that are in the map. First posted on arXiv 23 Sep 2024.

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7 of 34 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Learned Step Size Quantizationcited by 628
  3. 2021I-BERT: Integer-only BERT Quantizationcited by 255
  4. 2018Training and Inference with Integers in Deep Neural Networkscited by 86
  5. 2019PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inferencecited by 61
  6. 2019Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integerscited by 33
  7. 2023X-Former: In-Memory Acceleration of Transformerscited by 25

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  1. 2025NeuroSim V1.5: Improved Software Backbone for Benchmarking Compute-in-Memory Accelerators with Device and Circuit-level Non-idealities
  2. 2026ChatNeuroSim: An LLM Agent Framework for Automated Compute-in-Memory Accelerator Deployment and Optimization
  3. 2025CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search
  4. 2026Mixed-Precision Training and Compilation for RRAM-based Computing-in-Memory Accelerators
  5. 2022QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality
  6. 2025WaferLLM: Large Language Model Inference at Wafer Scale
  7. 2025Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators
  8. 2025Systematic Exploration of Multi-core Architectures for Efficient LLM Serving using WaferAI-SIM