| Parallel multi channel convolution using general matrix multiplication A Vasudevan, A Anderson, D Gregg 2017 IEEE 28th International Conference on Application-specific Systems …, 2017 | 66 | 2017 |
| Low-memory gemm-based convolution algorithms for deep neural networks A Anderson, A Vasudevan, C Keane, D Gregg arXiv preprint arXiv:1709.03395, 2017 | 35 | 2017 |
| Optimal DNN primitive selection with partitioned boolean quadratic programming A Anderson, D Gregg Proceedings of the 2018 International Symposium on Code Generation and …, 2018 | 18 | 2018 |
| Automatic vectorization of interleaved data revisited A Anderson, A Malik, D Gregg ACM Transactions on Architecture and Code Optimization (TACO) 12 (4), 1-25, 2015 | 16 | 2015 |
| Vectorization of multibyte floating point data formats A Anderson, D Gregg 2016 International Conference on Parallel Architecture and Compilation …, 2016 | 15 | 2016 |
| Performance-oriented neural architecture search A Anderson, J Su, R Dahyot, D Gregg 2019 International Conference on High Performance Computing & Simulation …, 2019 | 6 | 2019 |
| Error analysis and improving the accuracy of Winograd convolution for deep neural networks B Barabasz, A Anderson, KM Soodhalter, D Gregg ACM Transactions on Mathematical Software (TOMS) 46 (4), 1-33, 2020 | 5 | 2020 |
| Bonseyes AI Pipeline—Bringing AI to You: End-to-end integration of data, algorithms, and deployment tools MD Prado, J Su, R Saeed, L Keller, N Vallez, A Anderson, D Gregg, ... ACM Transactions on Internet of Things 1 (4), 1-25, 2020 | 3 | 2020 |
| Efficient Multibyte Floating Point Data Formats Using Vectorization A Anderson, S Muralidharan, D Gregg IEEE Transactions on Computers 66 (12), 2081-2096, 2017 | 3 | 2017 |
| TASO: Time and Space Optimization for Memory-Constrained DNN Inference Y Wen, A Anderson, V Radu, MFP O'Boyle, D Gregg 2020 IEEE 32nd International Symposium on Computer Architecture and High …, 2020 | 2 | 2020 |
| Composition of Saliency Metrics for Channel Pruning with a Myopic Oracle K Persand, A Anderson, D Gregg arXiv preprint arXiv:2004.03376, 2020 | 1 | 2020 |
| POSTER: Space and Time Optimal DNN Primitive Selection with Integer Linear Programming Y Wen, A Anderson, V Radu, MFP O’Boyle, D Gregg 2019 28th International Conference on Parallel Architectures and Compilation …, 2019 | 1 | 2019 |
| Scalar Arithmetic Multiple Data: Customizable Precision for Deep Neural Networks A Anderson, M Doyle, D Gregg 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH), 61-68, 2019 | 1 | 2019 |
| Composition of Saliency Metrics for Pruning with a Myopic Oracle K Persand, A Anderson, D Gregg 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 753-759, 2020 | | 2020 |
| High-Performance Low-Memory Lowering: GEMM-based Algorithms for DNN Convolution A Anderson, A Vasudevan, C Keane, D Gregg 2020 IEEE 32nd International Symposium on Computer Architecture and High …, 2020 | | 2020 |
| A Taxonomy of Channel Pruning Signals in CNNs K Persand, A Anderson, D Gregg arXiv preprint arXiv:1906.04675, 2019 | | 2019 |
| Hardware and software performance in deep learning A Anderson, J Garland, Y Wen, B Barabasz, K Persand, A Vasudevan, ... Many-Core Computing: Hardware and Software, 141, 2019 | | 2019 |
| Improving the Accuracy of Winograd Convolution for Deep Neural Networks B BARABASZ, A ANDERSON, D GREGG arXiv preprint arXiv:1803.10986, 2018 | | 2018 |
| Improving accuracy of Winograd convolution for DNNs. B Barabasz, A Anderson, D Gregg CoRR, 2018 | | 2018 |
| Vectorization for Accelerated Gather/Scatter and Multibyte Data Formats A Anderson Trinity College Dublin, 2016 | | 2016 |
David GreggProfessor in Computer Science, Lero, Trinity College DublinBestätigte E-Mail-Adresse bei cs.tcd.ie
