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.

利用近似计算并将硬件近似集成到多层感知器（MLP）的训练过程中，为 Printed Electronics（PE）设计了一种基于遗传算法的硬件感知训练方法，实现了超过 5 倍的面积和功耗降低，同时优于最先进的近似和随机打印 MLPs。

利用离散基因训练实现打印 MLP 网络的嵌入式硬件逼近

Embedding Hardware Approximations in Discrete Genetic-based Training for  Printed MLPs

Printed Electronics (PE) feature distinct and remarkable characteristics that
make them a prominent technology for achieving true ubiquitous computing. This
is particularly relevant in application domains that require conformal and
ultra-low cost solutions, which have experienced limited penetration of
computing until now. Unlike silicon-based technologies, PE offer unparalleled
features such as non-recurring engineering costs, ultra-low manufacturing cost,
and on-demand fabrication of conformal, flexible, non-toxic, and stretchable
hardware. However, PE face certain limitations due to their large feature
sizes, that impede the realization of complex circuits, such as machine
learning classifiers. In this work, we address these limitations by leveraging
the principles of Approximate Computing and Bespoke (fully-customized) design.
We propose an automated framework for designing ultra-low power Multilayer
Perceptron (MLP) classifiers which employs, for the first time, a holistic
approach to approximate all functions of the MLP's neurons: multiplication,
accumulation, and activation. Through comprehensive evaluation across various
MLPs of varying size, our framework demonstrates the ability to enable
battery-powered operation of even the most intricate MLP architecture examined,
significantly surpassing the current state of the art.

本文提出了一种基于印刷电子技术的自动设计框架，通过引入近似计算和定制设计的原则，克服了印刷电子技术中复杂电路实现的限制，实现了超低功耗的多层感知器分类器，使得即使在最复杂的结构下，也能实现电池供电操作且显著超越目前的技术水平。

面向印刷多层感知机的乘积累加和激活的定制逼近

Bespoke Approximation of Multiplication-Accumulation and Activation  Targeting Printed Multilayer Perceptrons

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。