| KEYWORD |
Teaching Neuromorphic Hardware Fault Resiliency
Parole chiave ARTIFICIAL INTELLIGENCE, HARDWARE AND SOFTWARE, HARDWARE ARCHITECTURE, NEUROMORPHIC COMPUTING
Riferimenti STEFANO DI CARLO, ALESSANDRO SAVINO
Riferimenti esterni Enrico Magliano
Gruppi di ricerca DAUIN - GR-24 - SMILIES - reSilient coMputer archItectures and LIfE Sci
Tipo tesi RESEARCH / EXPERIMENTAL
Descrizione Neuromorphic hardware, while powerful, can be prone to faults due to hardware degradation, environmental conditions, or manufacturing imperfections.
The human brain functions reliably, even in small damages or disruptions, offering inspiration for fault-resilient systems.
For neuromorphic hardware to be deployed in safety-critical applications (autonomous systems, healthcare devices, etc.), it must learn to adapt and
continue functioning even in the presence of faults.
Develop novel training methods that use fault injection to teach neuromorphic hardware how to detect, adapt, and recover from faults, ensuring long-term reliability and robustness.
Scadenza validita proposta 12/12/2025
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