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Bradley McDanel
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2020 – today
- 2024
- [i12]Bradley McDanel:
AMUSD: Asynchronous Multi-Device Speculative Decoding for LLM Acceleration. CoRR abs/2410.17375 (2024) - 2023
- [c21]Surat Teerapittayanon, Marcus Z. Comiter, Bradley McDanel
, H. T. Kung:
StitchNet: Composing Neural Networks from Pre-Trained Fragments. ICMLA 2023: 61-68 - [c20]Bradley McDanel
Dynamic Patch Sampling for Efficient Training and Dynamic Inference in Vision Transformers. ICMLA 2023: 83-89 - [i11]Surat Teerapittayanon, Marcus Z. Comiter, Bradley McDanel, H. T. Kung:
StitchNet: Composing Neural Networks from Pre-Trained Fragments. CoRR abs/2301.01947 (2023) - 2022
- [c19]Sai Qian Zhang, Bradley McDanel
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding. HPCA 2022: 846-860 - [c18]Bradley McDanel
Accelerating DNN Training with Structured Data Gradient Pruning. ICPR 2022: 2293-2299 - [i10]Bradley McDanel, Helia Dinh, John Magallanes:
Accelerating DNN Training with Structured Data Gradient Pruning. CoRR abs/2202.00774 (2022) - [i9]Bradley McDanel, Chi Phuong Huynh:
Accelerating Vision Transformer Training via a Patch Sampling Schedule. CoRR abs/2208.09520 (2022) - 2021
- [c17]Sai Qian Zhang, Bradley McDanel
Training for multi-resolution inference using reusable quantization terms. ASPLOS 2021: 845-860 - [c16]Bradley McDanel, Sai Qian Zhang, H. T. Kung:
Saturation RRAM Leveraging Bit-Level Sparsity Resulting from Term Quantization. ISCAS 2021: 1-5 - [i8]Sai Qian Zhang, Bradley McDanel, H. T. Kung:
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding. CoRR abs/2110.15456 (2021) - 2020
- [c15]Hsiang-Tsung Kung, Bradley McDanel, Sai Qian Zhang:
Term quantization: furthering quantization at run time. SC 2020: 96 - [i7]H. T. Kung, Bradley McDanel, Sai Qian Zhang:
Term Revealing: Furthering Quantization at Run Time on Quantized DNNs. CoRR abs/2007.06389 (2020)
2010 – 2019
- 2019
- [c14]H. T. Kung, Bradley McDanel, Sai Qian Zhang, Xin Dong, Chih-Chiang Chen:
Maestro: A Memory-on-Logic Architecture for Coordinated Parallel Use of Many Systolic Arrays. ASAP 2019: 42-50 - [c13]H. T. Kung, Bradley McDanel, Sai Qian Zhang:
Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization. ASPLOS 2019: 821-834 - [c12]Bradley McDanel, Sai Qian Zhang, H. T. Kung, Xin Dong:
Full-stack optimization for accelerating CNNs using powers-of-two weights with FPGA validation. ICS 2019: 449-460 - [c11]H. T. Kung, Bradley McDanel, Sai Qian Zhang, C. T. Wang, Jin Cai, C. Y. Chen, Victor C. Y. Chang, M. F. Chen, Jack Yuan-Chen Sun, Douglas Yu:
Systolic Building Block for Logic-on-Logic 3D-IC Implementations of Convolutional Neural Networks. ISCAS 2019: 1-5 - [i6]Bradley McDanel, Sai Qian Zhang, H. T. Kung, Xin Dong:
Full-stack Optimization for Accelerating CNNs with FPGA Validation. CoRR abs/1905.00462 (2019) - 2018
- [c10]H. T. Kung, Bradley McDanel, Sai Qian Zhang:
Adaptive Tiling: Applying Fixed-size Systolic Arrays To Sparse Convolutional Neural Networks. ICPR 2018: 1006-1011 - [c9]H. T. Kung, Bradley McDanel, Sai Qian Zhang:
Mapping Systolic Arrays onto 3D Circuit Structures: Accelerating Convolutional Neural Network Inference. SiPS 2018: 330-336 - [i5]H. T. Kung, Bradley McDanel, Sai Qian Zhang:
Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization. CoRR abs/1811.04770 (2018) - 2017
- [c8]Bradley McDanel, Surat Teerapittayanon, H. T. Kung:
Embedded Binarized Neural Networks. EWSN 2017: 168-173 - [c7]Surat Teerapittayanon, Bradley McDanel, H. T. Kung:
Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices. ICDCS 2017: 328-339 - [c6]Bradley McDanel, Surat Teerapittayanon, H. T. Kung:
Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference. ICMLA 2017: 186-193 - [i4]Surat Teerapittayanon, Bradley McDanel, H. T. Kung:
BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks. CoRR abs/1709.01686 (2017) - [i3]Surat Teerapittayanon, Bradley McDanel, H. T. Kung:
Distributed Deep Neural Networks over the Cloud, the Edge and End Devices. CoRR abs/1709.01921 (2017) - [i2]Bradley McDanel, Surat Teerapittayanon, H. T. Kung:
Embedded Binarized Neural Networks. CoRR abs/1709.02260 (2017) - [i1]Bradley McDanel, Surat Teerapittayanon, H. T. Kung:
Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference. CoRR abs/1710.07830 (2017) - 2016
- [c5]Surat Teerapittayanon, Bradley McDanel, H. T. Kung:
BranchyNet: Fast inference via early exiting from deep neural networks. ICPR 2016: 2464-2469 - 2015
- [c4]Abhinav Jauhri
Outlier detection for large scale manufacturing processes. IEEE BigData 2015: 2771-2774 - [c3]Hsieh-Chung Chen, Marcus Z. Comiter, H. T. Kung, Bradley McDanel:
Sparse Coding Trees with application to emotion classification. CVPR Workshops 2015: 77-86 - [c2]H. T. Kung, Bradley McDanel, Surat Teerapittayanon:
PNNU: Parallel Nearest-Neighbor Units for Learned Dictionaries. LCPC 2015: 223-237 - [c1]Stephen J. Tarsa, Marcus Z. Comiter, Michael B. Crouse, Bradley McDanel, H. T. Kung:
Taming Wireless Fluctuations by Predictive Queuing Using a Sparse-Coding Link-State Model. MobiHoc 2015: 287-296
Coauthor Index
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last updated on 2024-11-28 20:34 CET by the dblp team
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