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arXiv · 2601.21737

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

Rebecca Pelke·Joel Klein·Jose Cubero-Cascante·Nils Bosbach·Jan Moritz Joseph·Rainer Leupers
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Abstract

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 %.

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Rebecca Pelke, Joel Klein, Jose Cubero-Cascante, Nils Bosbach, Jan Moritz Joseph, Rainer Leupers. 2026-01-29. Mixed-Precision Training and Compilation for RRAM-based Computing-in-Memory Accelerators. https://arxiv.org/abs/2601.21737

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