让印刷神经网络实现高精度低功耗，支持任意输入精度。
Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation
- 用多目标优化和整体近似法自动设计印刷三值神经网络。
- 面积缩小17倍，功耗降低59倍，精度损失低于5%。
- 首次实现印刷神经网络在电池供电下的稳定运行。
印刷电子为硅基系统之外的应用提供了有前景的替代方案，具备柔性、可拉伸性、贴合性和超低制造成本等特性。尽管印刷电子特征尺寸较大，印刷神经网络因其满足应用需求而受到关注，但实现复杂电路仍具挑战。本文弥合了印刷神经网络在分类精度与面积效率之间的差距，涵盖从模拟-数字接口（主要的面积与功耗瓶颈）到数字分类器的端到端系统设计与协同优化。我们提出一种自动化框架，用于设计任意输入精度的印刷三值神经网络，采用多目标优化与整体近似方法。所设计电路在面积上平均优于现有近似印刷神经网络17倍，功耗降低59倍，首次实现印刷-电池供电运行，且精度损失低于5%，同时考虑了模拟-数字接口开销。
原文摘要 · Abstract (English)
Printed electronics offer a promising alternative for applications beyond silicon-based systems, requiring properties like flexibility, stretchability, conformality, and ultra-low fabrication costs. Despite the large feature sizes in printed electronics, printed neural networks have attracted attention for meeting target application requirements, though realizing complex circuits remains challenging. This work bridges the gap between classification accuracy and area efficiency in printed neural networks, covering the entire processing-near-sensor system design and co-optimization from the analog-to-digital interface-a major area and power bottleneck-to the digital classifier. We propose an automated framework for designing printed Ternary Neural Networks with arbitrary input precision, utilizing multi-objective optimization and holistic approximation. Our circuits outperform existing approximate printed neural networks by 17x in area and 59x in power on average, being the first to enable printed-battery-powered operation with under 5% accuracy loss while accounting for analog-to-digital interfacing costs.
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