DeepAI
Log In Sign Up

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

04/01/2018

A Survey of Techniques for Dynamic Branch Prediction

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

A Lightweight Isolation Mechanism for Secure Branch Predictors

Recently exposed vulnerabilities reveal the necessity to improve the sec...
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...
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...
01/31/2023

Retrosynthetic Planning with Dual Value Networks

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

Identifying and Exploiting Sparse Branch Correlations for Optimizing Branch Prediction

Branch prediction is arguably one of the most important speculative mech...
11/10/2022

Column Generation for Optimization Problems in Communication Networks

Numerous communication networks are emerging to serve the various demand...