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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