Flexible Electronics (FE) offer distinct advantages, including mechanical flexibility and low process temperatures, enabling extremely low-cost production. To address the demands of applications such as smart sensors and wearables, flexible devices must be small and operate at low supply voltages. Additionally, target applications often require classifiers to operate directly on analog sensory input, necessitating the use of Analog to Digital Converters (ADCs) to process the sensory data. However, ADCs present serious challenges, particularly in terms of high area and power consumption, especially when considering stringent area and energy budget. In this work, we target common classifiers in this domain such as MLPs and SVMs and present a holistic approach to mitigate the elevated overhead of analog to digital interfacing in FE. First, we propose a novel design for Binary Search ADC that reduces area overhead 2X compared with the state-of-the-art Binary design and up to 5.4X compared with Flash ADC. Next, we present an in-training ADC optimization in which we keep the bare-minimum representations required and simplifying ADCs by removing unnecessary components. Our in-training optimization further reduces on average the area in terms of transistor count of the required ADCs by 5X for less than 1% accuracy loss.

翻译：柔性电子技术具有独特的优势，包括机械柔性和低工艺温度，能够实现极低成本的生产。为满足智能传感器和可穿戴设备等应用需求，柔性器件必须尺寸小且能在低电源电压下工作。此外，目标应用通常要求分类器能直接处理模拟传感输入，这需要使用模数转换器来处理传感数据。然而，模数转换器带来了严峻挑战，尤其是在高面积和高功耗方面，特别是在考虑严格面积和能量预算时。本研究针对该领域常见的分类器（如MLP和SVM），提出了一种整体性方法来缓解柔性电子中模数接口的高昂开销。首先，我们提出了一种新颖的二进制搜索模数转换器设计，与最先进的二进制设计相比，面积开销降低了2倍，与闪存模数转换器相比，最高可降低5.4倍。其次，我们提出了一种训练中模数转换器优化方法，通过保留所需的最低限度表示并移除不必要的组件来简化模数转换器。我们的训练中优化进一步将所需模数转换器的晶体管数量平均减少了5倍，而精度损失小于1%。