Embedding Hardware Approximations in Discrete Genetic-based Training for
  Printed MLPs
Florentia Afentaki Michael Hefenbrock Georgios Zervakis Mehdi B. Tahoori
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.