# Mixed-Precision Training and Compilation for RRAM-based Computing-in-Memory Accelerators

arXiv: https://arxiv.org/abs/2601.21737 · HTML: https://mlsanity.com/paper/2601.21737/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://mlsanity.com/paper/1606.06160/) (2016)
- [HAQ: Hardware-Aware Automated Quantization with Mixed Precision](https://mlsanity.com/paper/1811.08886/) (2018)
- [HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision](https://mlsanity.com/paper/1905.03696/) (2019)
- [On the Accuracy of Analog Neural Network Inference Accelerators](https://mlsanity.com/paper/2109.01262/) (2021)
- [Quantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment](https://mlsanity.com/paper/2202.05048/) (2022)
- [CINM (Cinnamon): A Compilation Infrastructure for Heterogeneous Compute In-Memory and Compute Near-Memory Paradigms](https://mlsanity.com/paper/2301.07486/) (2022)
- [Mapping of CNNs on multi-core RRAM-based CIM architectures](https://mlsanity.com/paper/2309.03805/) (2023)
- [TDO-CIM: Transparent Detection and Offloading for Computation In-memory](https://mlsanity.com/paper/2007.00060/) (2020)
- [CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory Accelerators](https://mlsanity.com/paper/2401.12428/) (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)
- [Evaluating the Scalability of Binary and Ternary CNN Workloads on RRAM-based Compute-in-Memory Accelerators](https://mlsanity.com/paper/2505.07490/) (2025)
- [CLSA-CIM: A Cross-Layer Scheduling Approach for Computing-in-Memory Architectures](https://mlsanity.com/paper/2401.07671/) (2024)
- [Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer](https://mlsanity.com/paper/2505.14303/) (2025)

## Built on by (most cited first, 1 of 1)

- [CanniUplift: A Holistic Framework for Mitigating Seller and Incentive Cannibalization in E-commerce Uplift Modeling](https://mlsanity.com/paper/2607.05242/) (2026)

## Abstract (arXiv)

Computing-in-Memory (CIM) accelerators are a promising solution for accelerating Machine Learning (ML) workloads, as they perform Matrix-Vector Multiplications (MVMs) on crossbar arrays directly in memory. Although the bit widths of the crossbar inputs and cells are very limited, most CIM compilers do not support quantization below 8 bit. As a result, a single MVM requires many compute cycles, and weights cannot be efficiently stored in a single crossbar cell. To address this problem, we propose a mixed-precision training and compilation framework for CIM architectures. The biggest challenge is the massive search space, that makes it difficult to find good quantization parameters. This is why we introduce a reinforcement learning-based strategy to find suitable quantization configurations that balance latency and accuracy. In the best case, our approach achieves up to a 2.48x speedup over existing state-of-the-art solutions, with an accuracy loss of only 0.086 %.