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arXiv · 2506.12610

OscNet v1.5: Energy Efficient Hopfield Network on CMOS Oscillators for Image Classification

Wenxiao Cai·Zongru Li·Iris Wang·Yu-Neng Wang·Thomas H. Lee
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Abstract

Machine learning has achieved remarkable advancements but at the cost of significant computational resources. This has created an urgent need for a novel and energy-efficient computational fabric and corresponding algorithms. CMOS Oscillator Networks (OscNet) is a brain inspired and specially designed hardware for low energy consumption. In this paper, we propose a Hopfield Network based machine learning algorithm that can be implemented on OscNet. The network is trained using forward propagation alone to learn sparsely connected weights, yet achieves an 8% improvement in accuracy compared to conventional deep learning models on MNIST dataset. OscNet v1.5 achieves competitive accuracy on MNIST and is well-suited for implementation using CMOS-compatible ring oscillator arrays with SHIL. In oscillator-based inference, we utilize only 24% of the connections used in a fully connected Hopfield network, with merely a 0.1% drop in accuracy. OscNet v1.5 relies solely on forward propagation and employs sparse connections, making it an energy-efficient machine learning pipeline designed for oscillator computing fabric. The repository for OscNet family is: https://github.com/RussRobin/OscNet .

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Wenxiao Cai, Zongru Li, Iris Wang, Yu-Neng Wang, Thomas H. Lee. 2025-06-14. OscNet v1.5: Energy Efficient Hopfield Network on CMOS Oscillators for Image Classification. https://arxiv.org/abs/2506.12610

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