HW-SW Optimization of DNNs for Privacy-preserving People Counting on
  Low-resolution Infrared Arrays
Matteo Risso Chen Xie Francesco Daghero Alessio Burrello Seyedmorteza Mollaei Marco Castellano Enrico Macii Massimo Poncino Daniele Jahier Pagliari
Low-resolution infrared (IR) array sensors enable people counting
applications such as monitoring the occupancy of spaces and people flows while
preserving privacy and minimizing energy consumption. Deep Neural Networks
(DNNs) have been shown to be well-suited to process these sensor data in an
accurate and efficient manner. Nevertheless, the space of DNNs' architectures
is huge and its manual exploration is burdensome and often leads to sub-optimal
solutions. To overcome this problem, in this work, we propose a highly
automated full-stack optimization flow for DNNs that goes from neural
architecture search, mixed-precision quantization, and post-processing, down to
the realization of a new smart sensor prototype, including a Microcontroller
with a customized instruction set. Integrating these cross-layer optimizations,
we obtain a large set of Pareto-optimal solutions in the 3D-space of energy,
memory, and accuracy. Deploying such solutions on our hardware platform, we
improve the state-of-the-art achieving up to 4.2x model size reduction, 23.8x
code size reduction, and 15.38x energy reduction at iso-accuracy.