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Siddharth Garg

Assistant Professor

    20042019
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    Research Output 2004 2019

    A Concentration of Measure Approach to Database De-anonymization

    Shirani, F., Garg, S. & Erkip, E., Jul 2019, 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 2748-2752 5 p. 8849392. (IEEE International Symposium on Information Theory - Proceedings; vol. 2019-July).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Concentration of Measure
    Necessary Conditions
    Law of large numbers
    Joint Distribution
    Converse

    BadNets: Evaluating Backdooring Attacks on Deep Neural Networks

    Gu, T., Liu, K., Dolan-Gavitt, B. & Garg, S., Jan 1 2019, In : IEEE Access. 7, p. 47230-47243 14 p., 8685687.

    Research output: Contribution to journalArticle

    Open Access
    Neural networks
    Classifiers
    Detectors
    Deep neural networks
    Graphics processing unit

    Compact: On-chip compression of activations for low power systolic array based CNN acceleration

    Zhang, J., Raj, P., Zarar, S., Ambardekar, A. & Garg, S., Oct 2019, In : ACM Transactions on Embedded Computing Systems. 18, 5s, a47.

    Research output: Contribution to journalArticle

    Systolic arrays
    Chemical activation
    Neural networks
    Particle accelerators
    Network layers

    Enabling Timing Error Resilience for Low-Power Systolic-Array Based Deep Learning Accelerators

    Zhang, J., Ghodsi, Z., Rangineni, K. & Garg, S., Jan 1 2019, (Accepted/In press) In : IEEE Design and Test.

    Research output: Contribution to journalArticle

    Systolic arrays
    Particle accelerators
    Energy efficiency
    Image recognition
    Speech recognition

    Fault-tolerant Systolic Array Based Accelerators for Deep Neural Network Execution

    Zhang, J. J., Basu, K. & Garg, S., Jan 1 2019, In : IEEE Design and Test.

    Research output: Contribution to journalArticle

    Systolic arrays
    Particle accelerators
    Tensors
    Processing
    Deep neural networks