Digital processing system
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Digital Processing System. Shuvra S. Bhattacharyya Department of Electrical and Computer Engineering, and Institute for Advanced Computer Studies University of Maryland College Park MD 20742 , (301)405-3638, With Neil Goldsman and Babis Papadopoulis.

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Digital Processing System

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Digital Processing System

Shuvra S. BhattacharyyaDepartment of Electrical and Computer Engineering, andInstitute for Advanced Computer Studies

University of MarylandCollege Park MD, (301)405-3638,

With Neil Goldsman and Babis Papadopoulis

Laboratory affilations: Digital Signal Processing Laboratory, VLSI Design Automation Laboratory, Embedded Systems Research Laboratory, Communications and Signal Processing Laboratory

Digital Processing Platform

  • Low power micro-controller

    • Small size for compact integration

    • Enables adaptation of node behavior with changing requirements, environmental characteristics, and network state

    • Enables experimentation with different algorithms and protocols

    • Enables use of energy saving processor modes and associated operating system functionality

  • Development of streamlined software implementations

    • Highly memory-constrained software implementations are required due to size and energy constraints

    • Leverage our previous work in synthesis of memory-efficient embedded software implementations

    • Employ formal programming models, and apply graph-theoretic analysis and optimization of program structure

    • Explore migration into ASIC or 3D-integrated system

Low power sleep mode

Check for new data

Fuse with prior data

Example of Software Structure


No new data

Periodic wake-up




Broadcast new data

Extract data


Need to update neighbors?

Protocol Set-up and System Configuration

  • Handshaking

  • Source channel coding

  • Integrate with transceiver to establish PLL timing

  • Establish error correction coding

  • Establish low-complexity decoding

  • Assign transmission power

  • Assign processing tasks to network nodes

System-level Optimization Example:Task Assignment Algorithms

  • Need to balance communication and computation throughout the network

  • Develop models of power consumption in network nodes and communication links

  • Develop task graph models of overall network functionality

  • Develop algorithms to embed task graph algorithm specifications into the network

    • Assign processing tasks to network nodes

    • Turn off idle nodes

    • Large design space

  • Explore evolutionary algorithms to optimize task graph embeddings


Phenotype space(Original search space)




Evolutionary Algorithms

Decoding function

Genetic operators

Genotype space(Genetic representation)


References: selected prior work related to embedded software optimization

  • N. K. Bambha, S. S. Bhattacharyya, J. Teich, and E. Zitzler. Systematic integration of parameterized local search in evolutionary algorithms. IEEE Transactions on Evolutionary Computation. To appear.

  • S. S. Bhattacharyya. Hardware/software co-synthesis of DSP systems. In Y. H. Hu, editor, Programmable Digital Signal Processors: Architecture, Programming, and Applications, pages 333-378. Marcel Dekker, Inc., 2002.

  • P. K. Murthy and S. S. Bhattacharyya. Shared buffer implementations of signal processing systems using lifetime analysis techniques. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 20(2):177-198, February 2001.

  • S. S. Bhattacharyya, R. Leupers, and P. Marwedel. Software synthesis and code generation for DSP. IEEE Transactions on Circuits and Systems --- II: Analog and Digital Signal Processing, 47(9):849-875, September 2000.

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