Reflections on the memory wall

By Sally A. McKee. ACM International Conference on Computing Frontiers, 2004

452 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/f6b3ea152aa5d35056c677000085333621c54962

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In Electrical and Electronic Engineering

In Hardware and Architecture

Citations in the corpus

Logic-in-Memory Based on an Atomically Thin Semiconductor (2020, unknown): “Since these specific applications require both high-performance and energy-efficient computation, the power 13 and memory 14 constraints imposed by Von-Neumann computers, with separate processing and storage units, limit standard processors from meeting optimal requirements for these applications…”
Processing Data Where It Makes Sense: Enabling In-Memory Computation (2019, background): “Unfortunately, it has become increasingly difficult in recent years to scale all of these dimensions [1, 2, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], and the main memory bottleneck has instead been worsening.”
AI and Memory Wall (2024, unknown): “They predicted that the diverging speed of improvement of how fast computations can be performed versus how fast data can be fetched is going to create a “memory wall” issue [25, 42].”
Horton Tables: Fast Hash Tables for In-Memory Data-Intensive Computing (2016, background): “However, although BCHTs are higher-performing than other open addressing schemes [60, 64], we find that as the performance of modern computing systems becomes increasingly constrained by memory bandwidth [1, 11, 52, 70], they too suffer from a number of inefficiencies that originate from how data is”
Neuro-Inspired Computing With Emerging Nonvolatile Memorys (2018, background): “data movement between the microprocessor and off-chip memory/storage has become the bottleneck of the entire system [30].”