Printed Electronics (PE) stands out as a promisingtechnology for widespread computing due to its distinct attributes, such as low costs and flexible manufacturing. Unlike traditional silicon-based technologies, PE enables stretchable, conformal,and non-toxic hardware. However, PE are constrained by larger feature sizes, making it challenging to implement complex circuits such as machine learning (ML) classifiers. Approximate computing has been proven to reduce the hardware cost of ML circuits such as Multilayer Perceptrons (MLPs). In this paper, we maximize the benefits of approximate computing by integrating hardware approximation into the MLP training process. Due to the discrete nature of hardware approximation, we propose and implement a genetic-based, approximate, hardware-aware training approach specifically designed for printed MLPs. For a 5% accuracy loss, our MLPs achieve over 5x area and power reduction compared to the baseline while outperforming state of-the-art approximate and stochastic printed MLPs.

翻译：印刷电子（PE）因其低成本与柔性制造等特性，在普适计算领域展现出广阔前景。与传统硅基技术不同，PE可实现可拉伸、共形且无毒的硬件。然而，受限于较大的特征尺寸，PE难以实现机器学习分类器等复杂电路。近似计算已被证实可有效降低多层感知器（MLP）等机器学习电路的硬件成本。本文通过将硬件近似集成到MLP训练过程中，最大化近似计算的优势。针对硬件近似的离散特性，我们提出并实现了一种基于遗传算法的近似感知训练方法，专为印刷式MLP设计。在精度损失5%的条件下，我们的MLP相比基线电路实现了超过5倍的面积与功耗缩减，且性能优于当前最先进的近似与随机印刷式MLP。