Branch Prediction as a Reinforcement Learning Problem: Why, How and Case Studies

06/25/2021
by   Anastasios Zouzias, et al.
0

Recent years have seen stagnating improvements to branch predictor (BP) efficacy and a dearth of fresh ideas in branch predictor design, calling for fresh thinking in this area. This paper argues that looking at BP from the viewpoint of Reinforcement Learning (RL) facilitates systematic reasoning about, and exploration of, BP designs. We describe how to apply the RL formulation to branch predictors, show that existing predictors can be succinctly expressed in this formulation, and study two RL-based variants of conventional BPs.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/01/2018

A Survey of Techniques for Dynamic Branch Prediction

Branch predictor (BP) is an essential component in modern processors sin...
research
05/17/2020

A Lightweight Isolation Mechanism for Secure Branch Predictors

Recently exposed vulnerabilities reveal the necessity to improve the sec...
research
08/18/2022

Visual Explanation of Deep Q-Network for Robot Navigation by Fine-tuning Attention Branch

Robot navigation with deep reinforcement learning (RL) achieves higher p...
research
04/16/2021

Towards Standardizing Reinforcement Learning Approaches for Stochastic Production Scheduling

Recent years have seen a rise in interest in terms of using machine lear...
research
01/31/2023

Retrosynthetic Planning with Dual Value Networks

Retrosynthesis, which aims to find a route to synthesize a target molecu...
research
07/28/2022

Identifying and Exploiting Sparse Branch Correlations for Optimizing Branch Prediction

Branch prediction is arguably one of the most important speculative mech...
research
10/18/2021

Branch Predicting with Sparse Distributed Memories

Modern processors rely heavily on speculation to keep the pipeline fille...

Please sign up or login with your details

Forgot password? Click here to reset