Wenbo Zhao

According to our database, Wenbo Zhao authored at least 15 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

ERA-BS: Boosting the Efficiency of ReRAM-Based PIM Accelerator With Fine-Grained Bit-Level Sparsity.

Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Xiaoyao Liang, Li Jiang

IEEE Trans. Computers, September, 2024

2023

SIMSnn: A Weight-Agnostic ReRAM-based Search-In-Memory Engine for SNN Acceleration.

Fangxin Liu, Wenbo Zhao, Zongwu Wang, Xiaokang Yang, Li Jiang

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2023

2022

IVQ: In-Memory Acceleration of DNN Inference Exploiting Varied Quantization.

Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yilong Zhao, Tao Yang, Yiran Chen, Li Jiang

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022

Randomize and Match: Exploiting Irregular Sparsity for Energy Efficient Processing in SNNs.

Fangxin Liu, Zongwu Wang, Wenbo Zhao, Yongbiao Chen, Tao Yang, Xiaokang Yang, Li Jiang

Proceedings of the IEEE 40th International Conference on Computer Design, 2022

DynSNN: A Dynamic Approach to Reduce Redundancy in Spiking Neural Networks.

Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Fei Dai

Proceedings of the IEEE International Conference on Acoustics, 2022

SATO: spiking neural network acceleration via temporal-oriented dataflow and architecture.

Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Tao Yang, Zhezhi He, Xiaokang Yang, Li Jiang

Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022

EBSP: evolving bit sparsity patterns for hardware-friendly inference of quantized deep neural networks.

Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Zhezhi He, Naifeng Jing, Xiaoyao Liang, Li Jiang

Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022

PIM-DH: ReRAM-based processing-in-memory architecture for deep hashing acceleration.

Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Zhezhi He, Rui Yang, Qidong Tang, Tao Yang, Cheng Zhuo, Li Jiang

Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022

SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks.

Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Li Jiang

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network.

Fangxin Liu, Wenbo Zhao, Yilong Zhao, Zongwu Wang, Tao Yang, Zhezhi He, Naifeng Jing, Xiaoyao Liang, Li Jiang

CoRR, 2021

Improving Neural Network Efficiency via Post-training Quantization with Adaptive Floating-Point.

Fangxin Liu, Wenbo Zhao, Zhezhi He, Yanzhi Wang, Zongwu Wang, Changzhi Dai, Xiaoyao Liang, Li Jiang

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network.

Fangxin Liu, Wenbo Zhao, Zhezhi He, Zongwu Wang, Yilong Zhao, Tao Yang, Jingnai Feng, Xiaoyao Liang, Li Jiang

Proceedings of the 39th IEEE International Conference on Computer Design, 2021

Bit-Transformer: Transforming Bit-level Sparsity into Higher Preformance in ReRAM-based Accelerator.

Fangxin Liu, Wenbo Zhao, Zhezhi He, Zongwu Wang, Yilong Zhao, Yongbiao Chen, Li Jiang

Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2021

IM3A: Boosting Deep Neural Network Efficiency via In-Memory Addressing-Assisted Acceleration.

Fangxin Liu, Wenbo Zhao, Zongwu Wang, Tao Yang, Li Jiang

Proceedings of the GLSVLSI '21: Great Lakes Symposium on VLSI 2021, 2021

2020

AUSN: Approximately Uniform Quantization by Adaptively Superimposing Non-uniform Distribution for Deep Neural Networks.

Fangxin Liu, Wenbo Zhao, Yanzhi Wang, Changzhi Dai, Li Jiang

CoRR, 2020