| KEYWORD |
Convolutional Neural Network cores towards Deep Space
keywords AEROSPACE, ARTIFICIAL INTELLIGENCE, EMBEDDED SYSTEMS, NEURONAL NETWORK
Reference persons LUCA STERPONE
Research Groups GR-05 - ELECTRONIC CAD & RELIABILITY GROUP - CAD
Thesis type EXPERIMENTAL RESEARCH
Description Nowadays, the widespread adoption of Artificial Intelligence (AI) approaches in embedded systems paven the way of a new generation of computing cores able to outperform standard Huffman based computational approach. The goal of the present thesis is to develop a Neural Network circuit node able to implement Convolutional computation in low power and low area consumption and adopted in Deep Space missions. These missions are characterized by a long duration that is affecting the performances of a device (timing degradation) and consequently the computational capabilities. The thesis is done in collaboration with MicroChip and the European Space Agency.
Deadline 01/04/2021
PROPONI LA TUA CANDIDATURA