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Exploring Innovative Feedback Engines for Memory Prediction

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Exploring Innovative Feedback Engines for Memory Prediction.

Rel. Mariagrazia Graziano. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2023

Abstract:

Technology improvements and new architectural trends have led to a remarkable increase in processors’ performances. In particular, faster processor execution time requires memories to keep up with its speed. Otherwise, these would become the bottleneck in performance improvement. However, the technological progress has not yielded such a raise in memory performance, increasing the impact of memory latency as a bottleneck in performance improvements. In order to reduce the e

Relators: Mariagrazia Graziano
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 60
Additional Information: Tesi secretata. Fulltext non presente
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering)
Classe di laurea: New organization > Master science > LM-29 - ELECTRONIC ENGINEERING
Ente in cotutela: INSTITUT EURECOM (FRANCIA)
Aziende collaboratrici: ARM France SAS
URI: http://webthesis.biblio.polito.it/id/eprint/26692
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