Barabasz B, Anderson A, Soodhalter KM, Gregg D.  2020.  Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks. ACM Trans. Math. Softw.. 46

Anderson A, Vasudevan A, Keane C, Gregg D.  2020.  High-Performance Low-Memory Lowering: GEMM-based Algorithms for DNN Convolution. 2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD).

Wen Y, Anderson A, Radu V, O'Boyle M, Gregg D.  2020.  TASO: Time and Space Optimization for Memory-Constrained DNN Inference. 2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD).

Persand K, Anderson A, Gregg D.  2019.  A Taxonomy of Channel Pruning Signals in CNNs. CoRR. abs/1906.04675

Barabasz B, Anderson A, Gregg D.  2018.  Improving accuracy of Winograd convolution for DNNs. CoRR.

Anderson A, Gregg D.  2018.  Optimal DNN Primitive Selection with Partitioned Boolean Quadratic Programming. Proceedings of the 2018 International Symposium on Code Generation and Optimization.

Anderson A, Gregg D.  2018.  Scalar Arithmetic Multiple Data: Customizable Precision for Deep Neural Networks. CoRR. abs/1809.10572

Anderson A, Muralidharan S., Gregg D.  2017.  Efficient Multibyte Floating Point Data Formats Using Vectorization. IEEE Transactions on Computers. 66:2081-2096.

Vasudevan A, Anderson A, Gregg D.  2017.  Parallel Multi Channel convolution using General Matrix Multiplication. 2017 IEEE 28th International Conference on Application-specific Systems, Architectures and Processors (ASAP)2017 IEEE 28th International Conference on Application-specific Systems, Architectures and Processors (ASAP).

Anderson A, Gregg D.  2016.  Vectorization of Multibyte Floating Point Data Formats. Proceedings of the 2016 International Conference on Parallel Architectures and Compilation.

Anderson A, Malik A, Gregg D.  2015.  Automatic Vectorization of Interleaved Data Revisited. ACM Transactions on Architecture and Code Optimization. 12(4)