Printed electronics (PE) technology provides cost-effective hardware with
unmet customization, due to their low non-recurring engineering and fabrication
costs. PE exhibit features such as flexibility, stretchability, porosity, and
conformality, which make them a prominent candidate for enabling ubiquitous
computing. Still, the large feature sizes in PE limit the realization of
complex printed circuits, such as machine learning classifiers, especially when
processing sensor inputs is necessary, mainly due to the costly
analog-to-digital converters (ADCs). To this end, we propose the design of
fully customized ADCs and present, for the first time, a co-design framework
for generating bespoke Decision Tree classifiers. Our comprehensive evaluation
shows that our co-design enables self-powered operation of on-sensor printed
classifiers in all benchmark cases.

通过设计定制的 ADC，我们首次提出了一种共同设计框架，用于生成定制的决策树分类器，从而实现在所有基准案例中传感器上打印的分类器的自供电操作。

通过定制 ADC 和决策树协同设计在传感器上实现的机器学习分类

On-sensor Printed Machine Learning Classification via Bespoke ADC and  Decision Tree Co-Design

Deep reinforcement learning (DRL) has proven extremely useful in a large
variety of application domains. However, even successful DRL-based software can
exhibit highly undesirable behavior. This is due to DRL training being based on
maximizing a reward function, which typically captures general trends but
cannot precisely capture, or rule out, certain behaviors of the system. In this
paper, we propose a novel framework aimed at drastically reducing the
undesirable behavior of DRL-based software, while maintaining its excellent
performance. In addition, our framework can assist in providing engineers with
a comprehensible characterization of such undesirable behavior. Under the hood,
our approach is based on extracting decision tree classifiers from erroneous
state-action pairs, and then integrating these trees into the DRL training
loop, penalizing the system whenever it performs an error. We provide a
proof-of-concept implementation of our approach, and use it to evaluate the
technique on three significant case studies. We find that our approach can
extend existing frameworks in a straightforward manner, and incurs only a
slight overhead in training time. Further, it incurs only a very slight hit to
performance, or even in some cases - improves it, while significantly reducing
the frequency of undesirable behavior.

提出一种基于决策树分类器的框架，用于在 DRL 软件中大幅减少不良行为并提供工程师可理解的描述，且仅略微增加训练时间及对性能的影响。