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ORCIDAndrew Anderson 0001
Person information
- affiliation (PhD): Trinity College Dublin, Ireland
Other persons with the same name
- Andrew Anderson — disambiguation page
- Andrew Anderson 0002
![[0000-0003-4964-6059]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0003-4964-6059")
— Oregon State University, Corvallis, OR, USA
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Books and Theses
- 2016
[b1]Andrew Anderson:
Vectorization for accelerated gather/scatter and multibyte data formats. Trinity College Dublin, Ireland, 2016
Journal Articles
- 2022
- [j6]Syed Asad Alam, Andrew Anderson, Barbara Barabasz, David Gregg:
Winograd Convolution for Deep Neural Networks: Efficient Point Selection. ACM Trans. Embed. Comput. Syst. 21(6): 80:1-80:28 (2022) - 2021
- [j5]Kaveena Persand, Andrew Anderson, David Gregg:
Taxonomy of Saliency Metrics for Channel Pruning. IEEE Access 9: 120110-120126 (2021) - 2020
- [j4]Miguel de Prado, Jing Su, Rabia Saeed, Lorenzo Keller, Noelia Vállez, Andrew Anderson, David Gregg, Luca Benini, Tim Llewellynn, Nabil Ouerhani, Rozenn Dahyot, Nuria Pazos:
Bonseyes AI Pipeline - Bringing AI to You: End-to-end integration of data, algorithms, and deployment tools. ACM Trans. Internet Things 1(4): 26:1-26:25 (2020) - [j3]Barbara Barabasz, Andrew Anderson, Kirk M. Soodhalter, David Gregg:
Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks. ACM Trans. Math. Softw. 46(4): 37:1-37:33 (2020) - 2017
- [j2]Andrew Anderson, Servesh Muralidharan, David Gregg:
Efficient Multibyte Floating Point Data Formats Using Vectorization. IEEE Trans. Computers 66(12): 2081-2096 (2017) - 2016
- [j1]Andrew Anderson, Avinash Malik, David Gregg:
Automatic Vectorization of Interleaved Data Revisited. ACM Trans. Archit. Code Optim. 12(4): 50:1-50:25 (2016)
Conference and Workshop Papers
- 2021
- [c10]Kaveena Persand, Andrew Anderson, David Gregg:
Domino Saliency Metrics: Improving Existing Channel Saliency Metrics with Structural Information. AI*IA 2021: 447-461 - 2020
- [c9]Andrew Anderson, Aravind Vasudevan, Cormac Keane, David Gregg:
High-Performance Low-Memory Lowering: GEMM-based Algorithms for DNN Convolution. SBAC-PAD 2020: 99-106 - [c8]Yuan Wen, Andrew Anderson, Valentin Radu, Michael F. P. O'Boyle, David Gregg:
TASO: Time and Space Optimization for Memory-Constrained DNN Inference. SBAC-PAD 2020: 199-208 - [c7]Kaveena Persand, Andrew Anderson, David Gregg:
Composition of Saliency Metrics for Pruning with a Myopic Oracle. SSCI 2020: 753-759 - 2019
- [c6]Yuan Wen, Andrew Anderson, Valentin Radu, Michael F. P. O'Boyle, David Gregg:
POSTER: Space and Time Optimal DNN Primitive Selection with Integer Linear Programming. PACT 2019: 489-490 - [c5]Andrew Anderson, Michael Doyle, David Gregg:
Scalar Arithmetic Multiple Data: Customizable Precision for Deep Neural Networks. ARITH 2019: 61-68 - [c4]Andrew Anderson, Jing Su, Rozenn Dahyot, David Gregg:
Performance-Oriented Neural Architecture Search. HPCS 2019: 177-184 - 2018
- [c3]Andrew Anderson, David Gregg:
Optimal DNN primitive selection with partitioned boolean quadratic programming. CGO 2018: 340-351 - 2017
- [c2]Aravind Vasudevan, Andrew Anderson, David Gregg:
Parallel Multi Channel convolution using General Matrix Multiplication. ASAP 2017: 19-24 - 2016
- [c1]Andrew Anderson, David Gregg:
Vectorization of Multibyte Floating Point Data Formats. PACT 2016: 363-372
Informal and Other Publications
- 2022
- [i12]Syed Asad Alam, Andrew Anderson, Barbara Barabasz, David Gregg:
Winograd Convolution for Deep Neural Networks: Efficient Point Selection. CoRR abs/2201.10369 (2022) - [i11]Kaveena Persand, Andrew Anderson, David Gregg:
Domino Saliency Metrics: Improving Existing Channel Saliency Metrics with Structural Information. CoRR abs/2205.02131 (2022) - 2020
- [i10]Andrew Anderson, Jing Su, Rozenn Dahyot, David Gregg:
Performance-Oriented Neural Architecture Search. CoRR abs/2001.02976 (2020) - [i9]Kaveena Persand, Andrew Anderson, David Gregg:
Composition of Saliency Metrics for Channel Pruning with a Myopic Oracle. CoRR abs/2004.03376 (2020) - [i8]Yuan Wen, Andrew Anderson, Valentin Radu, Michael F. P. O'Boyle, David Gregg:
TASO: Time and Space Optimization for Memory-Constrained DNN Inference. CoRR abs/2005.10709 (2020) - 2019
- [i7]Kaveena Persand, Andrew Anderson, David Gregg:
A Taxonomy of Channel Pruning Signals in CNNs. CoRR abs/1906.04675 (2019) - 2018
- [i6]Barbara Barabasz, Andrew Anderson, David Gregg:
Improving accuracy of Winograd convolution for DNNs. CoRR abs/1803.10986 (2018) - [i5]Andrew Anderson, David Gregg:
Scalar Arithmetic Multiple Data: Customizable Precision for Deep Neural Networks. CoRR abs/1809.10572 (2018) - 2017
- [i4]Aravind Vasudevan, Andrew Anderson, David Gregg:
Parallel Multi Channel Convolution using General Matrix Multiplication. CoRR abs/1704.04428 (2017) - [i3]Andrew Anderson, Aravind Vasudevan, Cormac Keane, David Gregg:
Low-memory GEMM-based convolution algorithms for deep neural networks. CoRR abs/1709.03395 (2017) - [i2]Andrew Anderson, David Gregg:
Optimal DNN Primitive Selection with Partitioned Boolean Quadratic Programming. CoRR abs/1710.01079 (2017) - 2016
- [i1]Andrew Anderson, David Gregg:
Vectorization of Multibyte Floating Point Data Formats. CoRR abs/1601.07789 (2016)
Coauthor Index
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