Zongwu Wang

According to our database, Zongwu Wang authored at least 19 papers between 2021 and 2024.

Collaborative distances:

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

2024

SPARK: Scalable and Precision-Aware Acceleration of Neural Networks via Efficient Encoding.

Fangxin Liu, Ning Yang, Haomin Li, Zongwu Wang, Zhuoran Song, Songwen Pei, Li Jiang

Proceedings of the IEEE International Symposium on High-Performance Computer Architecture, 2024

2023

SoBS-X: Squeeze-Out Bit Sparsity for ReRAM-Crossbar-Based Neural Network Accelerator.

Fangxin Liu, Zongwu Wang, Yongbiao Chen, Zhezhi He, Tao Yang, Xiaoyao Liang, Li Jiang

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

Cross-layer Designs against Non-ideal Effects in ReRAM-based Processing-in-Memory System.

Chen Nie, Zongwu Wang, Qidong Tang, Chenyang Lv, Li Jiang, Zhezhi He

Proceedings of the 23rd International Symposium on Quality Electronic Design, 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

DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture.

Tao Yang, Dongyue Li, Zhuoran Song, Yilong Zhao, Fangxin Liu, Zongwu Wang, Zhezhi He, Li Jiang

Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition, 2022

Self-Terminating Write of Multi-Level Cell ReRAM for Efficient Neuromorphic Computing.

Zongwu Wang, Zhezhi He, Rui Yang, Shiquan Fan, Jie Lin, Fangxin Liu, Yueyang Jia, Chenxi Yuan, Qidong Tang, Li Jiang

Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition, 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

HAWIS: Hardware-Aware Automated WIdth Search for Accurate, Energy-Efficient and Robust Binary Neural Network on ReRAM Dot-Product Engine.

Qidong Tang, Zhezhi He, Fangxin Liu, Zongwu Wang, Yiyuan Zhou, Yinghuan Zhang, Li Jiang

Proceedings of the 27th Asia and South Pacific Design Automation Conference, 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