CMU-SAFARI/Pythia
A customizable hardware prefetching framework using online reinforcement learning as described in the MICRO 2021 paper by Bera et al. (https://arxiv.org/pdf/2109.12021.pdf).
This project offers a customizable framework for enhancing computer hardware's data prefetching capabilities. It uses reinforcement learning to intelligently anticipate and fetch data before it's explicitly requested, improving processor efficiency. By feeding it program context information, the system learns to make better prefetch decisions, resulting in faster and more efficient program execution. Hardware architects, computer engineers, and researchers working on processor and memory system design would find this valuable.
158 stars.
Use this if you are a hardware designer or researcher looking to evaluate and implement advanced, learning-based data prefetching strategies in processor architectures to improve system performance.
Not ideal if you are a software developer or end-user simply looking to improve application performance without engaging in detailed hardware simulation and design.
Stars
158
Forks
48
Language
C++
License
MIT
Category
Last pushed
Feb 21, 2026
Commits (30d)
0
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