Energy-optimal software partitioning in heterogeneous multiprocessor embedded systems

Embedded systems with heterogeneous processors extend the energy/timing trade-off flexibility and provide the opportunity to fine tune resource utilization for particular applications. In this paper, we present a resource model that considers the time and energy costs of run-time mode switching, which considerably improves the accuracy of existing models. Given an application, the software partitioning problem then becomes an optimization over energy cost given deadline constraints, which can be formulate as an integer linear programming (ILP) problem. We apply the resource modeling and software partitioning techniques to a multi- module embedded sensing device, the mPlatform, and present a case study of configuring the platform for a real-time sound source localization application on a stack of MSP430 and ARM7 processor based sensing and processing boards.

[1]  Xiliang Zhong,et al.  Frequency-aware energy optimization for real-time periodic and aperiodic tasks , 2007, LCTES '07.

[2]  Luca Benini,et al.  A survey of design techniques for system-level dynamic power management , 2000, IEEE Trans. Very Large Scale Integr. Syst..

[3]  David C. Snowdon,et al.  Power Management and Dynamic Voltage Scaling: Myths and Facts , 2005 .

[4]  Zhengyou Zhang,et al.  Maximum Likelihood Sound Source Localization for Multiple Directional Microphones , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[5]  Hakan Aydin,et al.  Energy-aware task allocation for rate monotonic scheduling , 2005, 11th IEEE Real Time and Embedded Technology and Applications Symposium.

[6]  Feng Zhao,et al.  mPlatform: A Flexible and Efficient Architecture for Sharing Data in Stack-Based Sensor Network Platforms , 2006 .

[7]  Andreas Savvides,et al.  XYZ: a motion-enabled, power aware sensor node platform for distributed sensor network applications , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[8]  Feng Zhao,et al.  mPlatform: A Reconfigurable Architecture and Efficient Data Sharing Mechanism for Modular Sensor Nodes , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[9]  Li Wang,et al.  A modular power-aware microsensor with >1000X dynamic power range , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[10]  W.J. Kaiser,et al.  The low power energy aware processing (LEAP) embedded networked sensor system , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[11]  Michael S. Brandstein,et al.  A robust method for speech signal time-delay estimation in reverberant rooms , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[12]  Pai H. Chou,et al.  Mode selection and mode-dependency modeling for power-aware embedded systems , 2002, Proceedings of ASP-DAC/VLSI Design 2002. 7th Asia and South Pacific Design Automation Conference and 15h International Conference on VLSI Design.

[13]  Anoop Gupta,et al.  Automating lecture capture and broadcast: technology and videography , 2004, Multimedia Systems.

[14]  Luca Benini,et al.  Dynamic frequency scaling with buffer insertion for mixed workloads , 2002, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[15]  Zhiyuan Li,et al.  Energy-Aware Scheduling for Real-Time Multiprocessor Systems with Uncertain Task Execution Time , 2007, 2007 44th ACM/IEEE Design Automation Conference.