A Retargetable Framework for Automated Discovery of Custom Instructions
Additional information
Authors
Bonzini P.,
Pozzi L.
Type
Article in conference proceedings
Year
2007
Language
English
Abstract
The problem of efficiently mapping a software application onto an extensible processor has received considerable attention. However, except for specialized kinds of computation accelerators, end-to-end studies of the problems are hard to find in the literature. We propose a classification of previous work on the mapping problem; we then frame previous results into this classification, and propose a new framework for solving this problem. By dividing the problem into several parts-some of them solved exactly, some of them relying on greedy algorithms-we provide a generic scheme that can be adapted to different kinds of hardware accelerators. We implemented our approach on top of a GCC-based compiler toolchain for extensible processors. Benchmarks taken from MiBench show a speedups up to 6.74 x using the SimpleScalar/ARM cycle-exact simulator.
Keywords
Acceleration, Application software, ARM cycle-exact simulator, Automatic programming, compiler toolchain, computation accelerators, custom instruction discovery, Embedded system, extensible processors, greedy algorithms, Hardware, hardware accelerators, Informatics, instruction set extension, instruction sets, Marketing and sales, MiBench, Partitioning algorithms, Performance gain, Power generation economics, program compilers, retargetable framework, SimpleScalar, software application mapping
Conference proceedings
Application -specific Systems, Architectures and Processors, 2007. ASAP. IEEE International Conf. on
Pages (or article number)
334-341
