Complex library mapping for embedded software using symbolic algebra

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Complex library mapping for embedded software using symbolic algebra

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Annual ACM IEEE Design Automation Conference archive
Proceedings of the 39th annual Design Automation Conference table of contents

New Orleans, Louisiana, USA

SESSION: Embedded software automation: from specification to binary table of contents

Pages: 325 - 330

Year of Publication: 2002

ISBN ~ ISSN:0738-100X , 1-58113-461-4

Authors

Armita Peymandoust	Computer Systems Laboratory Stanford University, Stanford, CA
Giovanni De Micheli	Computer Systems Laboratory Stanford University, Stanford, CA
Tajana Simunic	HP Labs & Stanford University, Palo Alto, CA

Sponsor

SIGDA: ACM Special Interest Group on Design Automation

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 2, Downloads (12 Months): 14, Citation Count: 1

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ABSTRACT

Embedded software designers often use libraries that have been pre-optimized for a given processor to achieve higher code quality. However, using such libraries in legacy code optimization is nontrivial and typically requires manual intervention. This paper presents a methodology that maps algorithmic constructs of the software specification to a library of complex software elements. This library-mapping step is automated by using symbolic algebra techniques. We illustrate the advantages of our methodology by optimizing an algorithmic level description of MPEG Layer III (MP3) audio decoder for the Badge4 [2] portable embedded system. During the optimization process we use commercially available libraries with complex elements ranging from simple mathematical functions such as exp to the IDCT routine. We implemented and measured the performance and energy consumption of the MP3 decoder software on Badge4 running embedded Linux operating system. The optimized MP3 audio decoder runs 300 times faster than the original code obtained from the standards body while consuming 400 times less energy. Since our optimized MP3 decoder runs 3.5 times faster than real-time, additional energy can be saved by using processor frequency and voltage scaling.

REFERENCES

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