|Radio frequency fingerprinting on the edge|
T Jian, Y Gong, Z Zhan, R Shi, N Soltani, Z Wang, J Dy, K Chowdhury, ...
IEEE Transactions on Mobile Computing 21 (11), 4078-4093, 2021
|Achieving on-mobile real-time super-resolution with neural architecture and pruning search|
Z Zhan*, Y Gong*, P Zhao*, G Yuan, W Niu, Y Wu, T Zhang, M Jayaweera, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
|Mest: Accurate and fast memory-economic sparse training framework on the edge|
G Yuan, X Ma, W Niu, Z Li, Z Kong, N Liu, Y Gong, Z Zhan, C He, Q Jin, ...
Advances in Neural Information Processing Systems 34, 20838-20850, 2021
|Npas: A compiler-aware framework of unified network pruning and architecture search for beyond real-time mobile acceleration|
Z Li, G Yuan, W Niu, P Zhao, Y Li, Y Cai, X Shen, Z Zhan, Z Kong, Q Jin, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
|A privacy-preserving-oriented dnn pruning and mobile acceleration framework|
Y Gong, Z Zhan, Z Li, W Niu, X Ma, W Wang, B Ren, C Ding, X Lin, X Xu, ...
Proceedings of the 2020 on Great Lakes Symposium on VLSI, 119-124, 2020
|Sparcl: Sparse continual learning on the edge|
Z Wang*, Z Zhan*, Y Gong, G Yuan, W Niu, T Jian, B Ren, S Ioannidis, ...
|A unified dnn weight pruning framework using reweighted optimization methods|
T Zhang, X Ma, Z Zhan, S Zhou, C Ding, M Fardad, Y Wang
2021 58th ACM/IEEE Design Automation Conference (DAC), 493-498, 2021
|SS-Auto: A single-shot, automatic structured weight pruning framework of DNNs with ultra-high efficiency|
Z Li, Y Gong, X Ma, S Liu, M Sun, Z Zhan, Z Kong, G Yuan, Y Wang
arXiv preprint arXiv:2001.08839, 2020
|Universal approximation property and equivalence of stochastic computing-based neural networks and binary neural networks|
Y Wang, Z Zhan, L Zhao, J Tang, S Wang, J Li, B Yuan, W Wen, X Lin
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5369-5376, 2019
|Blk-rew: A unified block-based dnn pruning framework using reweighted regularization method|
X Ma, Z Li, Y Gong, T Zhang, W Niu, Z Zhan, P Zhao, J Tang, X Lin, B Ren, ...
arXiv preprint arXiv:2001.08357, 2020
|Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution|
Y Wu, Y Gong, P Zhao, Y Li, Z Zhan, W Niu, H Tang, M Qin, B Ren, ...
|Towards real-time DNN inference on mobile platforms with model pruning and compiler optimization|
W Niu, P Zhao, Z Zhan, X Lin, Y Wang, B Ren
Proceedings of the Twenty-Ninth International Joint Conference on Artificial …, 2020
|Chic experience-driven scheduling in machine learning clusters|
Y Gong, B Li, B Liang, Z Zhan
Proceedings of the International Symposium on Quality of Service, 1-10, 2019
|Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration|
Y Gong, G Yuan, Z Zhan, W Niu, Z Li, P Zhao, Y Cai, S Liu, B Ren, X Lin, ...
ACM Transactions on Design Automation of Electronic Systems (TODAES) 27 (5), 2021
|A unified dnn weight compression framework using reweighted optimization methods|
T Zhang, X Ma, Z Zhan, S Zhou, M Qin, F Sun, YK Chen, C Ding, ...
arXiv preprint arXiv:2004.05531, 2020
|Blcr: Towards real-time dnn execution with block-based reweighted pruning|
X Ma, G Yuan, Z Li, Y Gong, T Zhang, W Niu, Z Zhan, P Zhao, N Liu, ...
2022 23rd International Symposium on Quality Electronic Design (ISQED), 1-8, 2022
|DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning|
Z Wang*, Z Zhan*, Y Gong, Y Shao, S Ioannidis, Y Wang, J Dy
|All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management|
Y Gong*, Z Zhan*, P Zhao, Y Wu, C Wu, C Ding, W Jiang, M Qin, Y Wang
2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 1-8, 2022
|Condense: A Framework for Device and Frequency Adaptive Neural Network Models on the Edge|
Y Gong, P Zhao, Z Zhan, Y Wu, C Wu, Z Kong, M Qin, C Ding, Y Wang
2023 60th ACM/IEEE Design Automation Conference (DAC), 1-6, 2023
|Computer-implemented methods and systems for privacy-preserving deep neural network model compression|
Y Wang, Y Gong, Z Zhan
US Patent App. 17/176,340, 2021