A linear complexity algorithm for the automatic generation of convex multiple input multiple output instructions

The instruction-set extensions problem has been one of the major topics in recent 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...

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Bibliographic Details
Published in:International journal of electronics Vol. 95; no. 7; pp. 603 - 619
Main Authors: Galuzzi, Carlo, Bertels, Koen, Vassiliadis✝, Stamatis
Format: Journal Article Conference Proceeding
Language:English
Published: London Taylor & Francis 01.07.2008
Subjects:
algorithms
Applied sciences
architecture
Circuit properties
Electric, optical and optoelectronic circuits
Electronic circuits
Electronics
Exact sciences and technology
HW/SW partioning
reconfigurable
Signal convertors
reconfigurable
algorithms
Instruction sets
Algorithm performance
Codesign
Automatic generation
Decoding circuit
Reconfigurable architectures
HW/SW partioning
Algorithm
architecture
ISSN:0020-7217, 1362-3060
Online Access:Get full text
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Summary:The instruction-set extensions problem has been one of the major topics in recent 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 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 ×2.9 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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ISSN:0020-7217
1362-3060
DOI:10.1080/00207210801923620