The Experts below are selected from a list of 7551 Experts worldwide ranked by ideXlab platform

Juinndar Huang - One of the best experts on this subject based on the ideXlab platform.

  • reducing fault Dictionary Size for million gate large circuits
    ACM Transactions on Design Automation of Electronic Systems, 2009
    Co-Authors: Yuru Hong, Juinndar Huang
    Abstract:

    In general, fault Dictionary is prevented from practical applications in fault diagnosis due to its extremely large Size. Several previous works are proposed for the fault Dictionary Size reduction. However, some of them fail to bring down the Size to an acceptable level, and others might not be able to handle today's million-gate circuits due to their high time and space complexity. In this article, an algorithm is presented to reduce the Size of pass-fail Dictionary while still preserving high diagnostic resolution. The proposed algorithm possesses low time and space complexity by avoiding constructing the huge distinguishability table, which inevitably boosts up the required computation complexity. Experimental results demonstrate that the proposed algorithm is capable of handling industrial million-gate large circuits in a reasonable amount of runtime and memory.

  • fault Dictionary Size reduction for million gate large circuits
    Asia and South Pacific Design Automation Conference, 2007
    Co-Authors: Yuru Hong, Juinndar Huang
    Abstract:

    In general, fault Dictionary is prevented from practical applications for its extremely large Size. Several previous works are proposed for the fault Dictionary Size reduction. However, they might not be able to handle today's million-gate circuits due to the high time and space complexity. In this paper, we propose an algorithm to significantly reduce the Size of fault Dictionary while still preserving high diagnostic resolution. The proposed algorithm possesses extremely low time and space complexity by avoiding constructing the huge distinguishability table, which inevitably boosts up the required computation complexity. Experimental results demonstrate that the proposed algorithm is fully capable of handling industrial million-gate large circuits in a reasonable amount of runtime and memory.

Antony Joseph - One of the best experts on this subject based on the ideXlab platform.

  • least squares superposition codes of moderate Dictionary Size are reliable at rates up to capacity
    IEEE Transactions on Information Theory, 2012
    Co-Authors: Antony Joseph, Andrew R. Barron
    Abstract:

    For the additive white Gaussian noise channel with average codeword power constraint, coding methods are analyzed in which the codewords are sparse superpositions, that is, linear combinations of subsets of vectors from a given design, with the possible messages indexed by the choice of subset. Decoding is by least squares (maximum likelihood), tailored to the assumed form of codewords being linear combinations of elements of the design. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.

  • least squares superposition codes of moderate Dictionary Size reliable at rates up to capacity
    arXiv: Information Theory, 2010
    Co-Authors: Andrew R. Barron, Antony Joseph
    Abstract:

    For the additive white Gaussian noise channel with average codeword power constraint, new coding methods are devised in which the codewords are sparse superpositions, that is, linear combinations of subsets of vectors from a given design, with the possible messages indexed by the choice of subset. Decoding is by least squares, tailored to the assumed form of linear combination. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.

  • least squares superposition codes of moderate Dictionary Size reliable at rates up to capacity
    International Symposium on Information Theory, 2010
    Co-Authors: Andrew R. Barron, Antony Joseph
    Abstract:

    Sparse superposition codes are developed for the additive white Gaussian noise channel with average codeword power constraint. Codewords are linear combinations of subsets of vectors, with the possible messages indexed by the choice of subset. Decoding is by least squares, tailored to the assumed form of linear combination. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.

Andrew R. Barron - One of the best experts on this subject based on the ideXlab platform.

  • least squares superposition codes of moderate Dictionary Size are reliable at rates up to capacity
    IEEE Transactions on Information Theory, 2012
    Co-Authors: Antony Joseph, Andrew R. Barron
    Abstract:

    For the additive white Gaussian noise channel with average codeword power constraint, coding methods are analyzed in which the codewords are sparse superpositions, that is, linear combinations of subsets of vectors from a given design, with the possible messages indexed by the choice of subset. Decoding is by least squares (maximum likelihood), tailored to the assumed form of codewords being linear combinations of elements of the design. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.

  • least squares superposition codes of moderate Dictionary Size reliable at rates up to capacity
    arXiv: Information Theory, 2010
    Co-Authors: Andrew R. Barron, Antony Joseph
    Abstract:

    For the additive white Gaussian noise channel with average codeword power constraint, new coding methods are devised in which the codewords are sparse superpositions, that is, linear combinations of subsets of vectors from a given design, with the possible messages indexed by the choice of subset. Decoding is by least squares, tailored to the assumed form of linear combination. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.

  • least squares superposition codes of moderate Dictionary Size reliable at rates up to capacity
    International Symposium on Information Theory, 2010
    Co-Authors: Andrew R. Barron, Antony Joseph
    Abstract:

    Sparse superposition codes are developed for the additive white Gaussian noise channel with average codeword power constraint. Codewords are linear combinations of subsets of vectors, with the possible messages indexed by the choice of subset. Decoding is by least squares, tailored to the assumed form of linear combination. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.

Yuru Hong - One of the best experts on this subject based on the ideXlab platform.

  • reducing fault Dictionary Size for million gate large circuits
    ACM Transactions on Design Automation of Electronic Systems, 2009
    Co-Authors: Yuru Hong, Juinndar Huang
    Abstract:

    In general, fault Dictionary is prevented from practical applications in fault diagnosis due to its extremely large Size. Several previous works are proposed for the fault Dictionary Size reduction. However, some of them fail to bring down the Size to an acceptable level, and others might not be able to handle today's million-gate circuits due to their high time and space complexity. In this article, an algorithm is presented to reduce the Size of pass-fail Dictionary while still preserving high diagnostic resolution. The proposed algorithm possesses low time and space complexity by avoiding constructing the huge distinguishability table, which inevitably boosts up the required computation complexity. Experimental results demonstrate that the proposed algorithm is capable of handling industrial million-gate large circuits in a reasonable amount of runtime and memory.

  • fault Dictionary Size reduction for million gate large circuits
    Asia and South Pacific Design Automation Conference, 2007
    Co-Authors: Yuru Hong, Juinndar Huang
    Abstract:

    In general, fault Dictionary is prevented from practical applications for its extremely large Size. Several previous works are proposed for the fault Dictionary Size reduction. However, they might not be able to handle today's million-gate circuits due to the high time and space complexity. In this paper, we propose an algorithm to significantly reduce the Size of fault Dictionary while still preserving high diagnostic resolution. The proposed algorithm possesses extremely low time and space complexity by avoiding constructing the huge distinguishability table, which inevitably boosts up the required computation complexity. Experimental results demonstrate that the proposed algorithm is fully capable of handling industrial million-gate large circuits in a reasonable amount of runtime and memory.

C.-p. Chung - One of the best experts on this subject based on the ideXlab platform.

  • Code compression by register operand dependency
    Journal of Systems and Software, 2004
    Co-Authors: Jyh-jiun J. Shann, C.-p. Chung
    Abstract:

    This paper proposes a Dictionary-based code compression technique that maps the source register operands to the nearest occurrence of a destination register in the predecessor instructions. The key idea is that most destination registers have a great possibility to be used as source registers in the following instructions. The dependent registers can be removed from the Dictionary if this information can be specified otherwise. Such destination source relationships are so common that making use of them can result in much better code compression. After removing the dependent register operands, the original Dictionary Size can be reduced significantly. As a result, the compression ratio can benefit from: (a) the reduction of Dictionary Size due to the removal of dependent registers, and (b) the reduction of program encoding due to the reduced number of Dictionary entries. A set of programs has been compressed using this feature. The compression results show that the average compression ratio is reduced to 38.41% on average for MediaBench benchmarks compiled for MIPS R2000 processor, as opposed to 45% using operand factorization.

  • Code compression by register operand dependency
    Proceedings Sixth Annual Workshop on Interaction between Compilers and Computer Architectures, 2002
    Co-Authors: Jyh-jiun J. Shann, C.-p. Chung
    Abstract:

    This paper proposes a Dictionary-based code compression technique that maps the source register operands to the nearest occurrence of a destination register in the predecessor instructions. The key idea is that most destination registers have great potential to be used as source registers in the following instructions. The dependent registers can be removed from the Dictionary if this information can be specified otherwise. As a result, the compression ratio benefits from the decreased Dictionary Size. A set of programs has been compressed using this feature. The compression results show that the average compression ratio is reduced to 38.6% on average for MediaBench benchmarks compiled for MIPS R2000 processor.