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A Linear Complexity Algorithm for the Automatic Generation of Convex Multiple Input Multiple Output Instructions

Authors: Carlo Galuzzi; Koen Bertels; Stamatis Vassiliadis✝;

A Linear Complexity Algorithm for the Automatic Generation of Convex Multiple Input Multiple Output Instructions

Abstract

The Instruction-Set Extensions problem has been one of the major topic in the last years and it consists of the addition of a set of new complex instructions to a given Instruction-Set. This problem in its general formulation requires an exhaustive search of the design space to identify the candidate instructions. This search turns into an exponential complexity of the solution. In this paper we propose an efficient linear complexity algorithm for the automatic generation of convex Multiple Input Multiple Output (MIMO) instructions, whose convexity is theoretically guaranteed. The proposed approach is not restricted to basic-block level and does not impose limitations either on the number of input and/or output, or on the number of new instructions generated. Our results show a significant overall application speedup (up to x2.9 for ADPCM decoder) considering the linear complexity of the proposed solution and which therefore compares well with other state-of-art algorithms for automatic instruction set extensions.

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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!
8
Average
Top 10%
Top 10%
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