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Huizi Mao
Huizi Mao
OmniML, Inc.
Verified email at omniml.ai - Homepage
Title
Cited by
Cited by
Year
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
S Han, H Mao, WJ Dally
arXiv preprint arXiv:1510.00149, 2015
103262015
EIE: Efficient inference engine on compressed deep neural network
S Han, X Liu, H Mao, J Pu, A Pedram, MA Horowitz, WJ Dally
ACM SIGARCH Computer Architecture News 44 (3), 243-254, 2016
31592016
Deep gradient compression: Reducing the communication bandwidth for distributed training
Y Lin, S Han, H Mao, Y Wang, WJ Dally
arXiv preprint arXiv:1712.01887, 2017
15112017
Trained ternary quantization
C Zhu, S Han, H Mao, WJ Dally
arXiv preprint arXiv:1612.01064, 2016
12802016
ESE: Efficient speech recognition engine with sparse lstm on fpga
S Han, J Kang, H Mao, Y Hu, X Li, Y Li, D Xie, H Luo, S Yao, Y Wang, ...
Proceedings of the 2017 ACM/SIGDA International Symposium on Field …, 2017
8132017
Bevfusion: Multi-task multi-sensor fusion with unified bird's-eye view representation
Z Liu, H Tang, A Amini, X Yang, H Mao, DL Rus, S Han
2023 IEEE international conference on robotics and automation (ICRA), 2774-2781, 2023
5692023
Exploring the granularity of sparsity in convolutional neural networks
H Mao, S Han, J Pool, W Li, X Liu, Y Wang, WJ Dally
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
495*2017
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv 2015
S Han, H Mao, WJ Dally
arXiv preprint arXiv:1510.00149, 0
338
DSD: Regularizing deep neural networks with dense-sparse-dense training flow
S Han, J Pool, S Narang, H Mao, S Tang, E Elsen, B Catanzaro, J Tran, ...
arXiv preprint arXiv:1607.04381, 2016
326*2016
Towards Real-Time Object Detection on Embedded Systems
Huizi Mao, Song Yao, Tianqi Tang, Boxun Li, Jun Yao, Yu Wang
IEEE Transactions on Emerging Topics in Computing 99 (99), 1-1, 2016
101*2016
Deep compression and EIE: Efficient inference engine on compressed deep neural network.
S Han, X Liu, H Mao, J Pu, A Pedram, M Horowitz, B Dally
Hot Chips Symposium, 1-6, 2016
592016
Vila: On pre-training for visual language models
J Lin, H Yin, W Ping, P Molchanov, M Shoeybi, S Han
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024
532024
A Delay Metric for Video Object Detection: What Average Precision Fails to Tell
H Mao, X Yang, WJ Dally
2019 International Conference on Computer Vision (ICCV), 2019
482019
Deep compression: Compressing deep neural network with pruning
S Han, H Mao, WJ Dally
Trained Quantization and Huffman Coding. arXiv 1510, v5, 2015
412015
CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video
H Mao, T Kong, WJ Dally
2019 The Conference on Systems and Machine Learning (SysML), 2019
292019
Real-time object detection towards high power efficiency
J Yu, K Guo, Y Hu, X Ning, J Qiu, H Mao, S Yao, T Tang, B Li, Y Wang, ...
2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 704-708, 2018
292018
Rebooting computing and low-power image recognition challenge
YH Lu, AM Kadin, AC Berg, TM Conte, EP DeBenedictis, R Garg, ...
2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 927-932, 2015
222015
PatchNet--Short-range Template Matching for Efficient Video Processing
H Mao, S Zhu, S Han, WJ Dally
arXiv preprint arXiv:2103.07371, 2021
92021
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation. arXiv 2022
Z Liu, H Tang, A Amini, X Yang, H Mao, D Rus, S Han
arXiv preprint arXiv:2205.13542, 0
9
Retrospective: EIE: Efficient Inference Engine on Sparse and Compressed Neural Network
S Han, X Liu, H Mao, J Pu, A Pedram, MA Horowitz, WJ Dally
arXiv preprint arXiv:2306.09552, 2023
22023
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