用串行支持向量机实现低功耗高精度柔性电子分类器
Compact Yet Highly Accurate Printed Classifiers Using Sequential Support Vector Machine Circuits
- 设计串行SVM电路，共享计算单元降低硬件复杂度
- 面积比现有印刷模型低6倍，准确率提升4.6%
- 适合超低成本柔性设备部署的定制化机器学习
印刷电子（PE）技术作为硅基计算的替代方案，具备按需超低制造成本、机械柔性与贴合性等优势。然而其大尺寸特征限制了复杂印刷机器学习分类器的实现。利用PE极低的非重复工程与制造成本，可针对特定模型和数据集完全定制硬件，显著降低电路复杂度。尽管已有进展，现有方案在面积效率上以大幅损失准确率为代价。本文通过设计专用控制与存储单元及单个乘加计算引擎，首次实现串行支持向量机（SVM）分类器，使印刷ML分类器在面积与功耗上更高效，且几乎无准确率损失。实验表明，所提SVM平均面积降低6倍，准确率提升4.6%，优于当前印刷领域最先进水平。
原文摘要 · Abstract (English)
Printed Electronics (PE) technology has emerged as a promising alternative to silicon-based computing. It offers attractive properties such as on-demand ultra-low-cost fabrication, mechanical flexibility, and conformality. However, PE are governed by large feature sizes, prohibiting the realization of complex printed Machine Learning (ML) classifiers. Leveraging PE's ultra-low non-recurring engineering and fabrication costs, designers can fully customize hardware to a specific ML model and dataset, significantly reducing circuit complexity. Despite significant advancements, state-of-the-art solutions achieve area efficiency at the expense of considerable accuracy loss. Our work mitigates this by designing area- and power-efficient printed ML classifiers with little to no accuracy degradation. Specifically, we introduce the first sequential Support Vector Machine (SVM) classifiers, exploiting the hardware efficiency of bespoke control and storage units and a single Multiply-Accumulate compute engine. Our SVMs yield on average 6x lower area and 4.6% higher accuracy compared to the printed state of the art.
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