RANGEL NETO, H. B.; http://lattes.cnpq.br/4128445658621635; RANGEL NETO, Humberto de Brito.
Abstract:
Embedded systems can be easily found in many domains of technology, from mobile
phones to satellites, from toys to industrial plants. As the name suggests, these systems
are embedded in more complex ones. And for this reason, many concerns arise from their
strict requirements of small size and low weight. When developing embedded systems,
these requirements are translated into constraints on memory, processing performance,
cost, and low energy consumption.
In the last decades, industry has joined forces with academic research for developing
new methods to increase energy efficiency of embedded systems, from hardware to
software, and to improve usability, cost, autonomy and reliability altogether.
The goal this project was to reduce the amount of transmitted data by applying
machine learning techniques for processing data on the edge. This strategy should increase
the autonomy of low power wireless applications.
This work presents a study case as a guide. A review of the technologies and of the
developed activities in the last months is presented. Among other topics, the process of
gathering, preprocessing and creating a database; defining, implementing and training a
neural network on CPU; the conversion from the CPU model to a model adapted to MCU
architecture; and the proposal and implementation of a solution adapted to low power in
a commercial MCU (NXP QN9080)
