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Offloading cache configuration prediction to an FPGA for hardware speedup and overhead reduction: work-in-progress

Authors:

Ruben Vazquez,

Ann Gordon-Ross,

Greg StittAuthors Info & Claims

CODES/ISSS '19: Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis Companion

Article No.: 11, Pages 1 - 2

https://doi.org/10.1145/3349567.3351730

Published: 13 October 2019 Publication History

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Abstract

In this paper, we present our cache configuration prediction methodology offloaded to an FPGA for improved performance and hardware overhead reduction, while maintaining cache configuration predictions within 5% of the optimal energy cache configuration for application phases for the instruction and data caches.

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Cited By

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Gupta DBansal AKaur JWadehra S(2023)Artificial Intelligence empowered content caching for energy optimization in vehicular networksProceedings of the 5th International Conference on Information Management & Machine Intelligence10.1145/3647444.3647847(1-4)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3647444.3647847
Wang SChen HWang Y(2022)Collaborative Caching for Energy Optimization in Content-Centric Internet of ThingsIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.30871979:1(230-238)Online publication date: Feb-2022
https://doi.org/10.1109/TCSS.2021.3087197

Offloading cache configuration prediction to an FPGA for hardware speedup and overhead reduction: work-in-progress
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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CODES/ISSS '19: Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis Companion

October 2019

64 pages

ISBN:9781450369237

DOI:10.1145/3349567

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 October 2019

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CODES/ISSS '19

CODES/ISSS '19: International Conference on Hardware/Software Codesign and System Synthesis

October 13 - 18, 2019

New York, New York

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Gupta DBansal AKaur JWadehra S(2023)Artificial Intelligence empowered content caching for energy optimization in vehicular networksProceedings of the 5th International Conference on Information Management & Machine Intelligence10.1145/3647444.3647847(1-4)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3647444.3647847
Wang SChen HWang Y(2022)Collaborative Caching for Energy Optimization in Content-Centric Internet of ThingsIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.30871979:1(230-238)Online publication date: Feb-2022
https://doi.org/10.1109/TCSS.2021.3087197

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References

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Increasing hardware data prefetching performance using the second-level cache

A highly configurable cache for low energy embedded systems

A highly configurable cache architecture for embedded systems

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