用隧穿二极管特性做激活函数，让神经网络更高效、更省电。
Full-Precision and Ternarised Neural Networks with Tunnel-Diode Activation Functions: Computing and Physics Perspectives
- 用隧穿二极管的电流-电压特性作为量子物理驱动的激活函数
- 在深层网络中实现更低损失和更高精度，优于传统激活函数
- 适合构建低功耗、类脑、三值化的硬件化AI系统
神经网络的数学复杂性和高维度导致训练与部署效率低下，需大量计算资源。为此，我们提出一种不同于当前主流神经与类脑计算系统的方案：利用隧穿二极管的电流-电压特性，作为基于量子物理的深度网络激活函数（TDAF）。该TDAF在深层架构中表现更优，实现了更低的损失与更高的准确率，同时展现出在面向类脑计算、三值化及能效优化的电子硬件中的应用潜力。本研究为机器学习、半导体电子学与量子物理搭建了新桥梁，融合了六次诺贝尔奖认可的量子隧穿现象（包括2025年获奖）与现代人工智能研究。
原文摘要 · Abstract (English)
The mathematical complexity and high dimensionality of neural networks slow both training and deployment, demanding heavy computational resources. This has driven the search for alternative architectures built from novel components, including new activation functions. Taking a different approach from state-of-the-art neural and neuromorphic computational systems, we employ the current-voltage characteristic of a tunnel diode as a quantum physics-based activation function for deep networks. This tunnel-diode activation function (TDAF) outperforms standard activations in deep architectures, delivering lower loss and higher accuracy in both training and evaluation. We also highlight its promise for implementation in electronic hardware aimed at neuromorphic, ternarised and energy efficient AI systems. Speaking broadly, our work lays a solid foundation for a new bridge between machine learning, semiconductor electronics and quantum physics -- bringing together quantum tunnelling, a phenomenon recognised in six Nobel Prizes (including the 2025 award), and contemporary AI research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。