| KEYWORD |
Microelectronics
Edge Performance Benchmarking for AI algorithms”
Thesis in external company
keywords MACHINE LEARNING, PATTERN RECOGNITION
Reference persons LUCIANO LAVAGNO
External reference persons Marcello Babbi, Reply Torino
Research Groups Microelectronics
Thesis type APPLIED RESEARCH
Description In edge computing scenarios, AI solution are widely used to augment devices intelligence, and
they are the most demanding in terms of HW characteristics. The thesis project aims to quantify
an optimize the performances of the most common AI techniques considering different hardware
and software different optimization strategies on edge MCU platforms.
Required skills Python programming, some knowledge of Machine Learning
Notes The student will be involved in:
❑ SW requirements definition;
❑ Edge platform capability assessment and selection;
❑ AI algorithms (e.g. Deep Neural Network) development in different applications (e.g. Battery Diagnostic);
❑ On edge performance optimization and compression methods development (e.g. clustering and spiking);
❑ Write a complete software library to optimize algorithms deployment on MCU platforms.
❑ Architecture experimental testing for benchmarking.
Deadline 11/10/2024
PROPONI LA TUA CANDIDATURA