Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware due to its additive manufacturing process, enabling machine learning (ML) applications for domains that feature ultra-low cost, conformity, and non-toxicity requirements that silicon-based systems cannot deliver. Nevertheless, large feature sizes in PE prohibit the realization of complex printed ML circuits. In this work, we present, for the first time, an automated printed-aware software/hardware co-design framework that exploits approximate computing principles to enable ultra-resource constrained printed multilayer perceptrons (MLPs). Our evaluation demonstrates that, compared to the state-of-the-art baseline, our circuits feature on average 6x (5.7x) lower area (power) and less than 1% accuracy loss.

翻译：印刷电子技术凭借其增材制造工艺，可提供按需、极低成本的硬件，从而在硅基系统无法实现的超低成本、共形性及无毒要求领域推动机器学习应用。然而，印刷电子技术中的大特征尺寸阻碍了复杂印刷机器学习电路的实现。本研究首次提出一种自动化的印刷感知软件/硬件协同设计框架，该框架利用近似计算原理实现超资源受限的印刷多层感知机。评估表明，与最先进基线相比，我们设计的电路平均面积（功耗）降低6倍（5.7倍），且精度损失小于1%。