Nicolo' Morando
Design of a distributed control unit for reconfigurable CNN accelerators.
Rel. Andrea Calimera, Valerio Tenace. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2018
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Abstract
Over the last few years, deep learning (DL) has evolved becoming per- vasive in many scientific and industrial fields. The effectiveness of DL techniques, aided by the widespread availability of user-friendly tools de- veloped by big ICT companies (like Google and Facebook, to name a few), is pushing the state-of-the-art in artificial intelligence, allowing Convolu- tional Neural Networks (CNNs) to represent a de facto standard for visual reasoning applications. CNNs are complex computational models inspired by the mechanisms that regulate the primary visual cortex of the brain, where images captured by the eyes are elaborated such to extrapolate a meaning, an information, from the surrounding environment (e.g., face re- cognition though feature detection).
A typical CNN structure is composed of an input layer handling images for computational stages, an output layer that produces the final answer on the classification task, and several hidden layers where the feature extraction takes place
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