Kevin Dule
Acceleration of Software Applications on FPGAs using High-Level Synthesis.
Rel. Luciano Lavagno, Mihai Teodor Lazarescu. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2025
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Abstract
We are living in a pivotal moment in history, witnessing a technological revolution unfold in real time, as the rise of AI rapidly reshapes the world around us. Due to increased data volumes, advanced algorithms, and improvements in computing power and storage, artificial intelligence and machine learning have soared in popularity today, causing extensive research to be dedicated to this field. A crucial component of these models are activation functions. These functions play a critical role in neural networks, as they enable AI/ML models to learn and represent complex patterns in data. However, due to their resource-intensive nature, more efficient solutions are needed.
This brings forth the main purpose of this thesis, which was to develop more efficient implementations of the activation functions currently used in the industry and accelerate them on FPGAs
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