| Branchynet: Fast inference via early exiting from deep neural networks S Teerapittayanon, B McDanel, HT Kung IAPR International Conference on Pattern Recognition, 2016 | 1368 | 2016 |
| Distributed Deep Neural Networks over the Cloud, the Edge and End Devices S Teerapittayanon, B McDanel, HT Kung 37th International Conference on Distributed Computing Systems (ICDCS 2017), 2017 | 975 | 2017 |
| Packing sparse convolutional neural networks for efficient systolic array implementations: Column combining under joint optimization HT Kung, B McDanel, SQ Zhang Proceedings of the Twenty-Fourth International Conference on Architectural …, 2019 | 200 | 2019 |
| Embedded Binarized Neural Networks B McDanel, S Teerapittayanon, HT Kung International Conference on Embedded Wireless Systems and Networks, 2017 | 123 | 2017 |
| FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding SQ Zhang, B McDanel, HT Kung The 28th IEEE International Symposium on High-Performance Computer …, 2022 | 68 | 2022 |
| Full-stack Optimization for Accelerating CNNs Using Powers-of-Two Weights with FPGA Validation B McDanel, SQ Zhang, HT Kung, X Dong International Conference on Supercomputing, 2019 | 45 | 2019 |
| Term Quantization: Furthering Quantization at Run Time HT Kung, B McDanel, SQ Zhang International Conference for High Performance Computing, Networking, Storage …, 2020 | 31* | 2020 |
| Maestro: A Memory-on-Logic Architecture for Coordinated Parallel Use of Many Systolic Arrays HT Kung, B McDanel, SQ Zhang, X Dong, CC Chen 2019 Application-specific Systems, Architectures and Processors, 2019 | 28 | 2019 |
| Adaptive Tiling: Applying Fixed-size Systolic Arrays To Sparse Convolutional Neural Networks HT Kung, B McDanel, SQ Zhang IAPR International Conference on Pattern Recognition, 2018 | 24 | 2018 |
| Mapping Systolic Arrays Onto 3D Circuit Structures: Accelerating Convolutional Neural Network Inference HT Kung, B McDanel, SQ Zhang IEEE Workshop on Signal Processing Systems, 2018 | 21 | 2018 |
| Systolic Building Block for Logic-on-Logic 3D-IC Implementations of Convolutional Neural Networks HT Kung, B McDanel, SQ Zhang, CT Wang, J Cai, CY Chen, VCY Chang, ... 2019 International Symposium on Circuits and Systems, 2019 | 15 | 2019 |
| Training for Multi-resolution Inference Using Reusable Quantization Terms SQ Zhang, B McDanel, HT Kung, X Dong Proceedings of the Twenty-Sixth International Conference on Architectural …, 2021 | 14 | 2021 |
| Saturation RRAM Leveraging Bit-level Sparsity Resulting from Term Quantization B McDanel, HT Kung, SQ Zhang 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2021 | 12 | 2021 |
| Incomplete dot products for dynamic computation scaling in neural network inference B McDanel, S Teerapittayanon, HT Kung 2017 16th IEEE International Conference on Machine Learning and Applications …, 2017 | 11 | 2017 |
| Sparse coding trees with application to emotion classification K Chen, MZ Comiter, HT Kung, B McDanel Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 9 | 2015 |
| Accelerating DNN Training with Structured Data Gradient Pruning B McDanel, H Dinh, J Magallanes 26th IAPR International Conference on Pattern Recognition, 2022 | 8 | 2022 |
| Taming wireless fluctuations by predictive queuing using a sparse-coding link-state model SJ Tarsa, M Comiter, MB Crouse, B McDanel, HT Kung Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc …, 2015 | 8 | 2015 |
| Outlier detection for large scale manufacturing processes A Jauhri, B McDanel, C Connor 2015 IEEE International Conference on Big Data (Big Data), 2771-2774, 2015 | 7 | 2015 |
| Revelata at the {F}in{LLM} Challenge Task: Improving Financial Text Summarization by Restricted Prompt Engineering and Fine-tuning K Kawamura, Z Li, CK Lin, B McDanel Proceedings of the Eighth Financial Technology and Natural Language …, 2024 | 2 | 2024 |
| Dynamic Patch Sampling for Efficient Training and Dynamic Inference in Vision Transformers B McDanel, CPN Huynh 2023 International Conference on Machine Learning and Applications (ICMLA …, 2023 | 2* | 2023 |
H. T. KungProfessor, Harvard University在 harvard.edu 的电子邮件经过验证
