Embedded program specialization for multiple criteria trade-offs

Improvement of characteristics of embedded system programs is important, because of limited resources available for applications in mobile devices. Decreasing power consumption is especially important because of limited battery life and slow growth of battery capacities. Here we analyze a problem of program specialization with multiple criteria (execution time, power, memory, accuracy) in mind. We propose a framework of the methodology for assessing multiple criteria in the problem domain and describe high-level models for evaluating embedded program characteristics using Feature Diagrams. To improve the characteristics of embedded software algorithms, we use function approximation and data specialization (look-up tables). In a case study we analyze the characteristics and trade-offs of the implementations of cosine function. Ill. 7, bibl. 23 (in English; summaries in English, Russian and Lithuanian).

[1]  Mahmut T. Kandemir,et al.  Influence of compiler optimizations on system power , 2001, IEEE Trans. Very Large Scale Integr. Syst..

[2]  Rami Melhem,et al.  Energy management for real-time embedded applications with compiler support , 2003 .

[3]  Peter Sestoft,et al.  Partial evaluation and automatic program generation , 1993, Prentice Hall international series in computer science.

[4]  Chandra Krintz,et al.  Application-level prediction of battery dissipation , 2004, Proceedings of the 2004 International Symposium on Low Power Electronics and Design (IEEE Cat. No.04TH8758).

[5]  Luca Benini,et al.  Source code transformation based on software cost analysis , 2001, International Symposium on System Synthesis (IEEE Cat. No.01EX526).

[6]  Andrew Wolfe,et al.  Compilation techniques for low energy: an overview , 1994, Proceedings of 1994 IEEE Symposium on Low Power Electronics.

[7]  Liviu Iftode,et al.  Context-aware Battery Management for Mobile Phones , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[8]  M. Manninger,et al.  Power management for portable devices , 2007, ESSCIRC 2007 - 33rd European Solid-State Circuits Conference.

[9]  Giovanni De Micheli,et al.  Low power embedded software optimization using symbolic algebra , 2002, Proceedings 2002 Design, Automation and Test in Europe Conference and Exhibition.

[10]  Xianfeng Li,et al.  Estimating the Worst-Case Energy Consumption of Embedded Software , 2006, 12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'06).

[11]  Erik Ruf,et al.  Data specialization , 1996, PLDI '96.

[12]  Robertas Damasevicius,et al.  Estimation of Power Consumption at Behavioral Modeling Level Using SystemC , 2007, EURASIP J. Embed. Syst..

[13]  Alan Jay Smith,et al.  Software strategies for portable computer energy management , 1998, IEEE Wirel. Commun..

[14]  Luca Benini,et al.  Source code optimization and profiling of energy consumption in embedded systems , 2000, ISSS '00.

[15]  Jason Flinn,et al.  Energy-aware adaptation for mobile applications , 1999, SOSP.

[16]  Assim Sagahyroon Power Consumption in Handheld Computers , 2006, APCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems.

[17]  Peter Y. H. Wong,et al.  An Investigation in Energy Consumption Analyses and Application−Level Prediction Techniques , 2006 .

[18]  Vytautas Štuikys,et al.  Metaprogramming techniques for designing embedded components for ambient intelligence , 2003 .