Memristor-based adaptive analog-to-digital conversion for efficient and accurate compute-in-memory

Haiqiao Hong, Zhiyuan Du, Mingrui Jiang, Ruibin Mao, Yuan Ren, Fuyi Li, Wei Mao, Muyuan Peng, Wei Zhang, Zhengwu Liu (), Can Li () and Ngai Wong ()
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Haiqiao Hong: The University of Hong Kong
Zhiyuan Du: The University of Hong Kong
Mingrui Jiang: The University of Hong Kong
Ruibin Mao: The University of Hong Kong
Yuan Ren: The University of Hong Kong
Fuyi Li: Xidian University
Wei Mao: Xidian University
Muyuan Peng: The University of Hong Kong
Wei Zhang: The Hong Kong University of Science and Technology
Zhengwu Liu: The University of Hong Kong
Can Li: The University of Hong Kong
Ngai Wong: The University of Hong Kong

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Compute-in-memory technology offers promising solutions for neural network acceleration but its potential is severely limited by inflexible and resource-intensive analog-to-digital converters. Here, we present a memristor-based analog-to-digital converter featuring adaptive quantization for diverse output distributions. Our design employs analog content-addressable memory cells with programmable overlapped boundaries to establish optimized quantization thresholds, demonstrating excellent integral and differential non-linearities. Extensive experiments validate the robustness of our approach by achieving 89.55% accuracy on CIFAR-10 (VGG8) at 5-bit adaptive quantized precision and maintaining competitive performance on ImageNet (ResNet18) through a proposed super-resolution strategy under experimental memristor variations. Compared to state-of-the-art designs, our converter achieves a 15.1× improvement in energy efficiency and a 12.9× reduction in area. Furthermore, integrating our converter into CIM systems reduces the energy and area overhead by up to 57.2% and 30.7%, respectively. This work establishes a paradigm for efficient and accurate signal quantization in practical compute-in-memory systems.

Date: 2025
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DOI: 10.1038/s41467-025-65233-w

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