A framework for branch predictor selection with aggregation on multiple parameters

Document Type

Conference Article

Publication Title

Communications in Computer and Information Science

Abstract

The performance of a branch predictor is measured not only by the prediction accuracy - parameters like predictor size, energy expenditure, latency of execution play a key role in predictor selection. The task of selecting the best predictor considering all the different parameters, is therefore, a non-trivial one, and is considered one of the foremost challenges. In this paper, we present a framework that systematically addresses this important challenge using the concept of aggregation and unification and makes a predictor selection based on different parameters. We present experimental results of our framework on the Siemens and SPEC 2006 benchmarks.

First Page

69

Last Page

74

DOI

10.1007/978-981-10-7470-7_8

Publication Date

1-1-2017

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