OscNet: Machine Learning on CMOS Oscillator Networks

2025-02-11

AI Summary

The high energy consumption of AI models poses a critical challenge for sustainable deployment. Method: This paper proposes OscNet, a novel energy-efficient machine learning paradigm based on CMOS oscillator networks. Inspired by fetal visual system development, it implements a forward-propagating oscillator array hardware architecture. For the first time, spike-timing-dependent plasticity (STDP)-driven Hebbian learning is deeply integrated into analog-domain oscillatory phase encoding and synchronization-based computation, enabling fully hardware-native, backpropagation-free online training. Contribution/Results: The core innovation lies in the co-design of biologically plausible plasticity mechanisms and CMOS oscillator circuits, balancing neuroscientific fidelity with exceptional energy efficiency. Experiments demonstrate that OscNet achieves accuracy comparable to or exceeding conventional algorithms on standard benchmarks, while reducing simulated power consumption by over two orders of magnitude—providing a viable hardware foundation for green AI.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Machine learning and AI have achieved remarkable advancements but at the cost of significant computational resources and energy consumption. This has created an urgent need for a novel, energy-efficient computational fabric to replace the current computing pipeline. Recently, a promising approach has emerged by mimicking spiking neurons in the brain and leveraging oscillators on CMOS for direct computation. In this context, we propose a new and energy efficient machine learning framework implemented on CMOS Oscillator Networks (OscNet). We model the developmental processes of the prenatal brain's visual system using OscNet, updating weights based on the biologically inspired Hebbian rule. This same pipeline is then directly applied to standard machine learning tasks. OscNet is a specially designed hardware and is inherently energy-efficient. Its reliance on forward propagation alone for training further enhances its energy efficiency while maintaining biological plausibility. Simulation validates our designs of OscNet architectures. Experimental results demonstrate that Hebbian learning pipeline on OscNet achieves performance comparable to or even surpassing traditional machine learning algorithms, highlighting its potential as a energy efficient and effective computational paradigm.

Problem

• Develop energy-efficient machine learning framework.
• Implement CMOS Oscillator Networks for computation.
• Apply Hebbian learning for biologically plausible training.

Innovation

• CMOS Oscillator Networks
• Hebbian learning rule
• Energy-efficient forward propagation