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Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load Prediction

Authors: Rahul Bera; Konstantinos Kanellopoulos; Shankar Balachandran; David Novo; Ataberk Olgun; Mohammad Sadrosadati; Onur Mutlu;

Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load Prediction

Abstract

Hermes is a speculative mechanism that accelerates long-latency off-chip load requests by removing on-chip cache access latency from their critical path. The key idea behind Hermes is to: (1) accurately predict which load requests might go to off-chip, and (2) speculatively start fetching the data required by the predicted off-chip loads directly from the main memory in parallel to the cache accesses. Hermes proposes a lightweight, perceptron-based off-chip predictor that identifies off-chip load requests using multiple disparate program features. The predictor is implemented using only tables and simple arithmetic operations like increment and decrement.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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12