Mattia Carlo Petruzzellis
Design of a Flexible Hardware Accelerator for Ultra-Low Power Quantized Neural Networks based on Serial Multipliers.
Rel. Maurizio Martina, Guido Masera, Francesco Conti. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2019
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Abstract
Today, our society is experiencing a new revolution that goes under the name of Artificial Intelligence (AI). In particular, the part of AI that is increasingly gaining more and more attention is Deep Learning (DL), whose main idea is to use a Deep Neural Network (DNN) in order to let a machine learn through training and perform, through inference, several tasks, as if these were performed by a human being. Among these tasks we find autonomous driving, speech recognition, computer vision and many others. The reason why DL is taking off compared to other well known and documented Machine Learning (ML) algorithms is due to its capability to take advantage of huge amount of data.
Indeed, whereas the latter have no considerable performance boost when increasing the available data over a certain threshold, the first can get huge benefits out of it the larger is the designed Neural Network (NN)
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