David Gregg
Academic
Professor David Gregg is an associate professor of computer science and Fellow of Trinity College Dublin. His research deals with software performance optimization, particularly for multicore and low-power embedded systems. He has successfully commercialized outputs from his research, and he works closely with companies such as Movidius and IBM Research. He currently serves as Head of Software Systems within Trinity College.
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2017. Bitslice Vectors: A Software Approach to Customizable Data Precision on Processors with SIMD Extensions. 2017 46th International Conference on Parallel Processing (ICPP).
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2017. Efficient Multibyte Floating Point Data Formats Using Vectorization. IEEE Transactions on Computers. 66:2081-2096.
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2017. Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks. IEEE Computer Architecture Letters. 16:132-135.
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2016. Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks. IEEE Computer Architecture Letters. PP
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2016. Vectorization of Multibyte Floating Point Data Formats. Proceedings of the 2016 International Conference on Parallel Architectures and Compilation.
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2016. Practical Algorithms for Finding Extremal Sets. J. Exp. Algorithmics. 21
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2016. Practical Algorithms for Finding Extremal Sets. J. Exp. Algorithmics. 21
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2016. Parallel Performance Problems on Shared-Memory Multicore Systems: Taxonomy and Observation. IEEE Transactions on Software Engineering. PP
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2015. Heuristics on Reachability Trees for Bicriteria Scheduling of Stream Graphs on Heterogeneous Multiprocessor Architectures. ACM Trans. Embedded Computer Systems. 14(2)
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2015. Exploiting Hyper-Loop Parallelism in Vectorization to Improve Memory Performance on CUDA GPGPU. Trustcom/BigDataSE/ISPA, 2015 IEEE.
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