# CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool

arXiv: https://arxiv.org/abs/2405.07259 · HTML: https://mlsanity.com/paper/2405.07259/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2024.

## Built on by (most cited first, 10 of 10)

- [Leveraging Compute-in-Memory for Efficient Generative Model Inference in TPUs](https://mlsanity.com/paper/2503.00461/) (2025)
- [Sorted Weight Sectioning for Energy-Efficient Unstructured Sparse DNNs on Compute-in-Memory Crossbars](https://mlsanity.com/paper/2410.11298/) (2024)
- [Towards Efficient IMC Accelerator Design Through Joint Hardware-Workload Co-optimization](https://mlsanity.com/paper/2410.16759/) (2024)
- [NeuroSim V1.5: Improved Software Backbone for Benchmarking Compute-in-Memory Accelerators with Device and Circuit-level Non-idealities](https://mlsanity.com/paper/2505.02314/) (2025)
- [CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search](https://mlsanity.com/paper/2509.25862/) (2025)
- [Systematic Exploration of Multi-core Architectures for Efficient LLM Serving using WaferAI-SIM](https://mlsanity.com/paper/2510.05632/) (2025)
- [Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators](https://mlsanity.com/paper/2603.03880/) (2026)
- [ChatNeuroSim: An LLM Agent Framework for Automated Compute-in-Memory Accelerator Deployment and Optimization](https://mlsanity.com/paper/2603.08745/) (2026)
- [ROSA: Robust and Energy-Efficient Microring-Based Optical Neural Networks via Optical Shift-and-Add and Layer-Wise Hybrid Mapping](https://mlsanity.com/paper/2605.00032/) (2026)
- [You Only Charge Once 2.0 : A End-to-End Analog Computing-in-Memory Architecture with Reconfigurable Switched Capacitors](https://mlsanity.com/paper/2608.11116/) (2026)

## Abstract (arXiv)

Compute-In-Memory (CiM) is a promising solution to accelerate Deep Neural Networks (DNNs) as it can avoid energy-intensive DNN weight movement and use memory arrays to perform low-energy, high-density computations. These benefits have inspired research across the CiM stack, but CiM research often focuses on only one level of the stack (i.e., devices, circuits, architecture, workload, or mapping) or only one design point (e.g., one fabricated chip). There is a need for a full-stack modeling tool to evaluate design decisions in the context of full systems (e.g., see how a circuit impacts system energy) and to perform rapid early-stage exploration of the CiM co-design space. To address this need, we propose CiMLoop: an open-source tool to model diverse CiM systems and explore decisions across the CiM stack. CiMLoop introduces (1) a flexible specification that lets users describe, model, and map workloads to both circuits and architecture, (2) an accurate energy model that captures the interaction between DNN operand values, hardware data representations, and analog/digital values propagated by circuits, and (3) a fast statistical model that can explore the design space orders-of-magnitude more quickly than other high-accuracy models. Using CiMLoop, researchers can evaluate design choices at different levels of the CiM stack, co-design across all levels, fairly compare different implementations, and rapidly explore the design space.