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High-Throughput Analysis of Optical Mapping Data Using ElectroMap
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Throughput analysis of digital partitioning with error-correcting codes for optical matrix-vector processors.

S A Ellett, T F Krile, J F Walkup

    Applied Optics
    |November 10, 2010
    PubMed
    Summary
    This summary is machine-generated.

    Digital partitioning and error-correcting codes enhance analog optical matrix-vector processor accuracy. Using one processor per submatrix optimizes throughput and hardware, with error-correcting codes showing minimal impact on performance.

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    Area of Science:

    • Optics and Photonics
    • Computer Science
    • Information Theory

    Background:

    • Analog optical matrix-vector processors offer potential for high-speed computation.
    • Achieving high accuracy in these systems remains a challenge.
    • Digital partitioning and error-correcting codes are proposed solutions.

    Purpose of the Study:

    • To analyze the throughput of digital partitioning and error-correcting codes in analog optical matrix-vector processors.
    • To determine the optimal system configuration for balancing throughput and hardware resources.
    • To evaluate the impact of error-correcting codes on system performance.

    Main Methods:

    • Detailed throughput analysis of the proposed technique.
    • Simulation and modeling of analog optical matrix-vector processors with digital partitioning.
    • Evaluation of error-correcting code overhead on system throughput.

    Main Results:

    • One processor per submatrix offers the best trade-off between system throughput and hardware requirements across various matrix sizes.
    • Error-correcting codes do not significantly reduce system throughput, especially for large matrices.
    • The technique demonstrates competitive performance compared to digital electronic computers.

    Conclusions:

    • Digital partitioning combined with error-correcting codes is an effective strategy for high-accuracy analog optical matrix-vector processing.
    • The optimal configuration involves dedicating one processor to each submatrix.
    • This approach presents a viable path towards high-performance optical computing systems.