RFVP

Author:

Yazdanbakhsh Amir1,Pekhimenko Gennady2,Thwaites Bradley1,Esmaeilzadeh Hadi1,Mutlu Onur2,Mowry Todd C.2

Affiliation:

1. Georgia Institute of Technology

2. Carnegie Mellon University

Abstract

This article aims to tackle two fundamental memory bottlenecks: limited off-chip bandwidth (bandwidth wall) and long access latency (memory wall). To achieve this goal, our approach exploits the inherent error resilience of a wide range of applications. We introduce an approximation technique, called Rollback-Free Value Prediction (RFVP). When certain safe-to-approximate load operations miss in the cache, RFVP predicts the requested values. However, RFVP does not check for or recover from load-value mispredictions, hence, avoiding the high cost of pipeline flushes and re-executions. RFVP mitigates the memory wall by enabling the execution to continue without stalling for long-latency memory accesses. To mitigate the bandwidth wall, RFVP drops a fraction of load requests that miss in the cache after predicting their values. Dropping requests reduces memory bandwidth contention by removing them from the system. The drop rate is a knob to control the trade-off between performance/energy efficiency and output quality. Our extensive evaluations show that RFVP, when used in GPUs, yields significant performance improvement and energy reduction for a wide range of quality-loss levels. We also evaluate RFVP’s latency benefits for a single core CPU. The results show performance improvement and energy reduction for a wide variety of applications with less than 1% loss in quality.

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture,Information Systems,Software

Reference56 articles.

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2. A Machine Learning Based Load Value Approximator Guided by the Tightened Value Locality;Proceedings of the Great Lakes Symposium on VLSI 2023;2023-06-05

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