Printed electronics technology offers a cost-effectiveand fully-customizable solution to computational needs beyondthe capabilities of traditional silicon technologies, offering ad-vantages such as on-demand manufacturing and conformal, low-cost hardware. However, the low-resolution fabrication of printedelectronics, which results in large feature sizes, poses a challengefor integrating complex designs like those of machine learn-ing (ML) classification systems. Current literature optimizes onlythe Multilayer Perceptron (MLP) circuit within the classificationsystem, while the cost of analog-to-digital converters (ADCs)is overlooked. Printed applications frequently require on-sensorprocessing, yet while the digital classifier has been extensivelyoptimized, the analog-to-digital interfacing, specifically the ADCs,dominates the total area and energy consumption. In this work,we target digital printed MLP classifiers and we propose thedesign of customized ADCs per MLP's input which involvesminimizing the distinct represented numbers for each input,simplifying thus the ADC's circuitry. Incorporating this ADCoptimization in the MLP training, enables eliminating ADC levelsand the respective comparators, while still maintaining highclassification accuracy. Our approach achieves 11.2x lower ADCarea for less than 5% accuracy drop across varying MLPs.

翻译：印刷电子技术为超越传统硅技术能力的计算需求提供了一种经济高效且完全可定制的解决方案，具备按需制造、共形贴合及低成本硬件等优势。然而，印刷电子器件的低分辨率制造工艺导致特征尺寸较大，这对集成机器学习分类系统等复杂设计提出了挑战。现有文献仅优化分类系统中的多层感知器电路，而忽视了模数转换器的成本。印刷应用常需片上传感器处理，尽管数字分类器已得到广泛优化，但模数接口（特别是模数转换器）仍占据总面积和能耗的主导地位。本研究针对数字印刷多层感知器分类器，提出为每个多层感知器输入设计定制化模数转换器的方案，通过最小化各输入端的独立表征数值来简化模数转换器电路。将这种模数转换器优化融入多层感知器训练过程，可在保持高分类精度的同时消除模数转换器量化层级及相应比较器。该方法在不同多层感知器架构中实现了模数转换器面积降低11.2倍，而精度损失低于5%。