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ORCIDWenbo Zhao 0005
Person information
- affiliation: Shanghai Jiao Tong University, China
- affiliation: Columbia University, School of Engineering and Applied Science, New York, NY, USA
Other persons with the same name
- Wenbo Zhao (aka: Wen-bo Zhao, Wen-Bo Zhao) — disambiguation page
- Wenbo Zhao 0001 — University of California, Department of Computer Science and Engineering, San Diego, CA, USA
- Wenbo Zhao 0002
![[0000-0002-3019-2328]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-3019-2328")
(aka: Wen-Bo Zhao 0002) — Nanyang Technological University, School of Computer Engineering, Singapore (and 1 more) - Wenbo Zhao 0003 — Chinese Academy of Sciences, Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Beijing, China (and 1 more)
- Wenbo Zhao 0004 — Peng Cheng Laboratory, Shenzhen, China (and 1 more)
- Wenbo Zhao 0006 — Amazon Alexa AI, CA, uSA (and 1 more)
- Wenbo Zhao 0007 — Soochow University, School of Mechanical and Electrical Engineering, Suzhou, China
- Wenbo Zhao 0008 — Lanzhou Jiaotong University, School of Traffic and Transportation, China
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Journal Articles
- 2022
[j1]Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yilong Zhao, Tao Yang, Yiran Chen, Li Jiang:
IVQ: In-Memory Acceleration of DNN Inference Exploiting Varied Quantization. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 41(12): 5313-5326 (2022)
Conference and Workshop Papers
- 2023
- [c11]Fangxin Liu, Wenbo Zhao, Zongwu Wang, Xiaokang Yang, Li Jiang:
SIMSnn: A Weight-Agnostic ReRAM-based Search-In-Memory Engine for SNN Acceleration. DATE 2023: 1-2 - 2022
- [c10]Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Li Jiang:
SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks. AAAI 2022: 1692-1701 - [c9]Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Zhezhi He, Naifeng Jing, Xiaoyao Liang, Li Jiang:
EBSP: evolving bit sparsity patterns for hardware-friendly inference of quantized deep neural networks. DAC 2022: 259-264 - [c8]Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Zhezhi He, Rui Yang, Qidong Tang, Tao Yang, Cheng Zhuo, Li Jiang:
PIM-DH: ReRAM-based processing-in-memory architecture for deep hashing acceleration. DAC 2022: 1087-1092 - [c7]Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Tao Yang, Zhezhi He, Xiaokang Yang, Li Jiang:
SATO: spiking neural network acceleration via temporal-oriented dataflow and architecture. DAC 2022: 1105-1110 - [c6]Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Fei Dai:
DynSNN: A Dynamic Approach to Reduce Redundancy in Spiking Neural Networks. ICASSP 2022: 2130-2134 - [c5]Fangxin Liu, Zongwu Wang, Wenbo Zhao, Yongbiao Chen, Tao Yang, Xiaokang Yang, Li Jiang:
Randomize and Match: Exploiting Irregular Sparsity for Energy Efficient Processing in SNNs. ICCD 2022: 451-454 - 2021
- [c4]Fangxin Liu, Wenbo Zhao, Zongwu Wang, Tao Yang, Li Jiang:
IM3A: Boosting Deep Neural Network Efficiency via In-Memory Addressing-Assisted Acceleration. ACM Great Lakes Symposium on VLSI 2021: 253-258 - [c3]Fangxin Liu, Wenbo Zhao, Zhezhi He, Zongwu Wang, Yilong Zhao, Yongbiao Chen, Li Jiang:
Bit-Transformer: Transforming Bit-level Sparsity into Higher Preformance in ReRAM-based Accelerator. ICCAD 2021: 1-9 - [c2]Fangxin Liu, Wenbo Zhao, Zhezhi He, Zongwu Wang, Yilong Zhao, Tao Yang, Jingnai Feng, Xiaoyao Liang, Li Jiang:
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network. ICCD 2021: 417-424 - [c1]Fangxin Liu, Wenbo Zhao, Zhezhi He, Yanzhi Wang, Zongwu Wang, Changzhi Dai, Xiaoyao Liang, Li Jiang:
Improving Neural Network Efficiency via Post-training Quantization with Adaptive Floating-Point. ICCV 2021: 5261-5270
Informal and Other Publications
- 2021
- [i2]Fangxin Liu, Wenbo Zhao, Yilong Zhao, Zongwu Wang, Tao Yang, Zhezhi He, Naifeng Jing, Xiaoyao Liang, Li Jiang:
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network. CoRR abs/2103.01705 (2021) - 2020
- [i1]Fangxin Liu, Wenbo Zhao, Yanzhi Wang, Changzhi Dai, Li Jiang:
AUSN: Approximately Uniform Quantization by Adaptively Superimposing Non-uniform Distribution for Deep Neural Networks. CoRR abs/2007.03903 (2020)
Coauthor Index
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