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Fast Custom Instruction Identification by Convex Subgraph Enumeration

Kubilay Atasu, Oskar Mencer, Wayne Luk, Can Ozturan, Gunhan Dundar

Conference or Workshop Paper
19th IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP), Leuven, Belgium.
July, 2008
Abstract

Automatic generation of custom instruction processors from high-level application descriptions enables fast design space exploration, while offering very favorable performance and silicon area combinations. This work introduces a novel method for adapting the instruction set to match an application captured in a high-level language. A simplified model is used to find the optimal instructions via enumeration of maximal convex subgraphs of application data flow graphs (DFGs). Our experiments involving a set of multimedia and cryptography benchmarks show that an order of magnitude performance improvement can be achieved using only a limited amount of hardware resources. In most cases, our algorithm takes less than a second to execute.

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