Alessandro Mandrile
Design of a high-performance, low-power data speculation memory subsystem.
Rel. Renato Ferrero. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
Abstract
The growing gap between processor and memory speeds has led to a series of prefetching techniques aimed at masking memory latency by exploiting spatial or temporal relationships in memory access patterns. However, highly efficient data retrieval techniques require a considerable amount of memory to avoid mispredictions and consequent memory loss. This internship proposal focuses on the study of a new approach to increasing the memory efficiency of our latest processor, and on the implementation of a new memory subsystem to be integrated into Arm's next-generation CPU.
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