Francesco La Carpia
Resilient Deep Neural Network for FPGA space applications.
Rel. Luciano Lavagno, Mario Roberto Casu, Mihai Teodor Lazarescu, Filippo Minnella. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2024
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Abstract
The effect of radiation on electronic devices can generate errors on various scales that can be catastrophic. In space missions, satellite devices are often used to collect numerous pieces of information on board before transmitting them to Earth. The loss of this information would lead to a high waste of resources and the failure of the entire space mission. The use of resilience techniques is aimed at preventing such errors in a radiation-rich environment such as space. The adoption of machine learning and artificial intelligence techniques in edge computing systems is growing due to their efficiency, reduced latency and enhanced decision-making capabilities.
Going into more detail, FPGAs are dataflow accelerators that compute operations in parallel, enabling the execution of numerous computations with low energy consumption
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