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Revisiting the Perception Predictor Again

Tarjan, David; Skadron, Kevin
Format
Report
Author
Tarjan, David
Skadron, Kevin
Abstract
We introduce a new kind of branch predictor, the hashed perceptron predictor, which merges the concepts behind the gshare and perceptron branch predictors. This is done by fetching the perceptron weights using the exclusive-or of branch addresses and branch history. This predictor can achieve superior accuracy to a path-based and a global perceptron predictor, previously the most accurate fully dynamic branch predictors known in the literature, at the same storage budgets. Additionally, it reduces the number of adders by a factor of four compared to a path-based perceptron. We also show how such a predictor can be ahead pipelined to yield one cycle effective latency, making it the first standalone perceptron predictor. On the SPEC integer set of benchmarks, the hashed ahead-pipelined path-based perceptron predictor (hashed perceptron for short) improves accuracy by 20% over a path-based perceptron and improves IPC by 5.8%. We believe these improvements make the perceptron predictor a promising choice as a branch predictor for a future high-performance microprocessor.
Language
English
Date Received
2012-10-29
Published
University of Virginia, Department of Computer Science, 2004
Published Date
2004
Rights
All rights reserved (no additional license for public reuse)
Collection
Libra Open Repository

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