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Design and optimization of Computational SRAM architectures dedicated to highly data-centric applications.
Rel. Carlo Ricciardi. Politecnico di Torino, Corso di laurea magistrale in Nanotechnologies For Icts (Nanotecnologie Per Le Ict), 2020
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Abstract
??I have undergone my Master Thesis at CEA Grenoble working on an emerging compu-ting architecture called Computational-SRAM (C-SRAM) based on SRAM memory technol-ogy which allows to perform calculations directly in memory without going through a proces-sor. These novel architectures are meant to be the most energy efficient possible to satisfy the needs of targeted data-centric and artificiel intelligence applications. ??In fact, an initial C-SRAM controller was already implemented by my team at CEA Grenoble. The aim of this internship was to optimize this controller by implementing new computing functionalities as well as reducing its energy consumption and its surface. The same work could be also be adapted to different types of memories.
??First, I had to familiarize with the existing design flow already developed by the la-boratory
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