Automated Precision Tuning in Activity Classification Systems

Nicola Fossati, Daniele Cattaneo, Michele Chiari, Stefano Cherubin, Giovanni Agosta
2020 Proceedings of the 11th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures / 9th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms  
The greater availability and reduction in production cost make wearable IoT platforms perfect candidates to continuously monitor people at risk, like elderly people. In particular these platforms, along with the use of artifical intelligence algorithms, can be exploited to detect and monitor people's activities, in particular potentially harmful situations, such as falling. However, wearable devices have limited computational power and battery life. We optimize a situation-recognition
more » ... n via the wellknown precision tuning practice using a dedicated state-of-the-art toolchain. After the optimization we evaluate how the reducedprecision version better fits the use case of limited-resources platforms, such as wearable devices. In particular, we achieve over 500% of speedup in execution time, and consume about 6 times less energy to carry out the classification. CCS CONCEPTS • Hardware → Power estimation and optimization; • Software and its engineering → Compilers; • Applied computing → Consumer health.
doi:10.1145/3381427.3381432 dblp:conf/hipeac/FossatiCCCA20 fatcat:sncrfcxg4vdolb6ymxflacpnvy