Manuel Capaccio
Reliability Analysis of Convolutional Neural Network through Soft Error Mitigation Controller.
Rel. Luca Sterpone, Sarah Azimi. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2023
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Abstract
The progresses of Deep Neural Networks (DNNs) in several disciplines like image processing, system monitoring and decision making, continue to accelerate, making them appealing for space applications, in particular if implemented on SRAM-based FPGAs, which offer several advantages, like low cost manufacturing, CPUs-like performance and field programmability. Therefore, radiation effects have to be considered in design phase. Ionizing particles can modify the state of gates in electronic devices, leading to permanent faults (Hard Errors) or temporary ones (Soft Errors). Single Event Upsets (SEU), one kind of soft error, can modify the value of one or more bits stored in the FPGA configuration memory, potentially causing design failures.
It is therefore important to be able to easily and cheaply verify the behavior of the design subjected to SEUs so as to be able to mitigate their effects if needed
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