ISEGEN: an iterative improvement-based ISE generation technique for fast customization of processors

Customization of processor architectures through instruction set extensions (ISEs) is an effective way to meet the growing performance demands of embedded applications. A high-quality ISE generation approach needs to obtain results close to those achieved by experienced designers, particularly for c...

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Bibliographic Details
Published in:IEEE transactions on very large scale integration (VLSI) systems 2006-07, Vol.14 (7), p.754-762
Main Authors: Biswas, P., Banerjee, S., Dutt, N.D., Pozzi, L., Ienne, P.
Format: Article
Language:English
Subjects:
Acceleration
Application-specific processors
Applied sciences
Computer architecture
Cryptography
Design. Technologies. Operation analysis. Testing
Electronics
Embedded computing
Embedded system
Equivalence
Exact sciences and technology
Flow graphs
Genetics
hardware-software partitioning
Informatics
instruction set extensions
Integrated circuits
Integrated circuits by function (including memories and processors)
Iterative methods
Manufacturing processes
Marketing
Optimization
Processors
Regularity
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
Studies
Very large scale integration
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Summary:Customization of processor architectures through instruction set extensions (ISEs) is an effective way to meet the growing performance demands of embedded applications. A high-quality ISE generation approach needs to obtain results close to those achieved by experienced designers, particularly for complex applications that exhibit regularity: expert designers are able to exploit manually such regularity in the data flow graphs to generate high-quality ISEs. In this paper, we present ISEGEN, an approach that identifies high-quality ISEs by iterative improvement following the basic principles of the well-known Kernighan-Lin min-cut heuristic. Experimental results on a number of MediaBench, EEMBC, and cryptographic applications show that our approach matches the quality of the optimal solution obtained by exhaustive search. We also show that our ISEGEN technique is on average 20times faster than a genetic formulation that generates equivalent solutions. Furthermore, the ISEs identified by our technique exhibit 35% more speedup than the genetic solution on a large cryptographic application by effectively exploiting its regular structure
ISSN:1063-8210
1557-9999
DOI:10.1109/TVLSI.2006.878345