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2020 – today
- 2025
[j11]Shiyuan Huang
, Fangxin Liu, Tian Li, Zongwu Wang, Ning Yang, Haoming Li, Li Jiang:
STCO: Enhancing Training Efficiency via Structured Sparse Tensor Compilation Optimization. ACM Trans. Design Autom. Electr. Syst. 30(1): 1-22 (2025)- 2024
- [j10]Fangxin Liu, Wenbo Zhao, Zongwu Wang, Yongbiao Chen, Xiaoyao Liang, Li Jiang:
ERA-BS: Boosting the Efficiency of ReRAM-Based PIM Accelerator With Fine-Grained Bit-Level Sparsity. IEEE Trans. Computers 73(9): 2320-2334 (2024) - [c42]Fangxin Liu, Yingjie Pei, Xuefei Zhang, Xiaofeng Tao:
Performance Analysis of ASTARS-Assisted Uplink Communication Networks. APCC 2024: 371-376 - [c41]Fangxin Liu, Haomin Li, Ning Yang, Yichi Chen, Zongwu Wang, Tao Yang, Li Jiang:
PAAP-HD: PIM-Assisted Approximation for Efficient Hyper-Dimensional Computing. ASPDAC 2024: 46-51 - [c40]Haomin Li, Fangxin Liu, Yichi Chen, Li Jiang:
HyperFeel: An Efficient Federated Learning Framework Using Hyperdimensional Computing. ASPDAC 2024: 716-721 - [c39]Fangxin Liu, Haomin Li, Ning Yang, Zongwu Wang, Tao Yang, Li Jiang:
TEAS: Exploiting Spiking Activity for Temporal-wise Adaptive Spiking Neural Networks. ASPDAC 2024: 842-847 - [c38]Shiyuan Huang, Fangxin Liu, Tian Li, Zongwu Wang, Haomin Li, Li Jiang:
TSTC: Enabling Efficient Training via Structured Sparse Tensor Compilation. ASPDAC 2024: 884-889 - [c37]Zhuoran Song, Chunyu Qi, Fangxin Liu, Naifeng Jing, Xiaoyao Liang:
CMC: Video Transformer Acceleration via CODEC Assisted Matrix Condensing. ASPLOS (2) 2024: 201-215 - [c36]Fangxin Liu, Ning Yang, Zhiyan Song, Zongwu Wang, Haomin Li, Shiyuan Huang, Zhuoran Song, Songwen Pei, Li Jiang:
INSPIRE: Accelerating Deep Neural Networks via Hardware-friendly Index-Pair Encoding. DAC 2024: 10:1-10:6 - [c35]Ning Yang, Fangxin Liu, Zongwu Wang, Haomin Li, Zhuoran Song, Songwen Pei, Li Jiang:
EOS: An Energy-Oriented Attack Framework for Spiking Neural Networks. DAC 2024: 58:1-58:6 - [c34]Xueyuan Liu, Zhuoran Song, Xiang Liao, Xing Li, Tao Yang, Fangxin Liu, Xiaoyao Liang:
Sava: A Spatial- and Value-Aware Accelerator for Point Cloud Transformer. DATE 2024: 1-6 - [c33]Jiahao Sun, Fangxin Liu, Yijian Zhang, Li Jiang, Rui Yang:
RTSA: An RRAM-TCAM based In-Memory-Search Accelerator for Sub-100 µs Collision Detection. DATE 2024: 1-2 - [c32]Fangxin Liu, Ning Yang, Haomin Li, Zongwu Wang, Zhuoran Song, Songwen Pei, Li Jiang:
SPARK: Scalable and Precision-Aware Acceleration of Neural Networks via Efficient Encoding. HPCA 2024: 1029-1042 - [c31]Yilong Zhao, Mingyu Gao, Fangxin Liu, Yiwei Hu, Zongwu Wang, Han Lin, Jin Li, He Xian, Hanlin Dong, Tao Yang, Naifeng Jing, Xiaoyao Liang, Li Jiang:
UM-PIM: DRAM-based PIM with Uniform & Shared Memory Space. ISCA 2024: 644-659 - [c30]Fangxin Liu, Shiyuan Huang, Longyu Zhao, Li Jiang, Zongwu Wang:
LowPASS: A Low power PIM-based accelerator with Speculative Scheme for SNNs. ISLPED 2024: 1-6 - [c29]Zhuoran Song, Houshu He, Fangxin Liu, Yifan Hao, Xinkai Song, Li Jiang, Xiaoyao Liang:
SRender: Boosting Neural Radiance Field Efficiency via Sensitivity-Aware Dynamic Precision Rendering. MICRO 2024: 525-537 - [c28]Zongwu Wang, Fangxin Liu, Ning Yang, Shiyuan Huang, Haomin Li, Li Jiang:
COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation. MICRO 2024: 1090-1106 - 2023
- [j9]Tao Yang, Dongyue Li, Fei Ma, Zhuoran Song, Yilong Zhao, Jiaxi Zhang, Fangxin Liu, Li Jiang:
PASGCN: An ReRAM-Based PIM Design for GCN With Adaptively Sparsified Graphs. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 42(1): 150-163 (2023) - [j8]Fangxin Liu, Zongwu Wang, Yongbiao Chen, Zhezhi He, Tao Yang, Xiaoyao Liang, Li Jiang:
SoBS-X: Squeeze-Out Bit Sparsity for ReRAM-Crossbar-Based Neural Network Accelerator. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 42(1): 204-217 (2023) - [j7]Tao Yang, Fei Ma, Xiaoling Li, Fangxin Liu, Yilong Zhao, Zhezhi He, Li Jiang:
DTATrans: Leveraging Dynamic Token-Based Quantization With Accuracy Compensation Mechanism for Efficient Transformer Architecture. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 42(2): 509-520 (2023) - [j6]Yongbiao Chen, Sheng Zhang, Fangxin Liu, Chenggang Wu, Kaicheng Guo, Zhengwei Qi:
DVHN: A Deep Hashing Framework for Large-Scale Vehicle Re-Identification. IEEE Trans. Intell. Transp. Syst. 24(9): 9268-9280 (2023) - [j5]Zhuoran Song, Wanzhen Liu, Tao Yang, Fangxin Liu, Naifeng Jing, Xiaoyao Liang:
A Point Cloud Video Recognition Acceleration Framework Based on Tempo-Spatial Information. IEEE Trans. Parallel Distributed Syst. 34(12): 3224-3237 (2023) - [c27]Fangxin Liu, Haoming Li, Yongbiao Chen, Tao Yang, Li Jiang:
HyperAttack: An Efficient Attack Framework for HyperDimensional Computing. DAC 2023: 1-6 - [c26]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 - [c25]Tao Yang, Hui Ma, Yilong Zhao, Fangxin Liu, Zhezhi He, Xiaoli Sun, Li Jiang:
PIMPR: PIM-based Personalized Recommendation with Heterogeneous Memory Hierarchy. DATE 2023: 1-6 - [c24]Haomin Li, Fangxin Liu, Yichi Chen, Li Jiang:
HyperNode: An Efficient Node Classification Framework Using HyperDimensional Computing. ICCAD 2023: 1-9 - [c23]Fangxin Liu, Ning Yang, Li Jiang:
PSQ: An Automatic Search Framework for Data-Free Quantization on PIM-based Architecture. ICCD 2023: 507-514 - 2022
- [j4]Zihan Jiang, Jiansong Li, Fangxin Liu, Wanling Gao, Lei Wang, Chuanxin Lan, Fei Tang, Lei Liu, Tao Li:
A systematic study on benchmarking AI inference accelerators. CCF Trans. High Perform. Comput. 4(2): 87-103 (2022) - [j3]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) - [c22]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 - [c21]Qidong Tang, Zhezhi He, Fangxin Liu, Zongwu Wang, Yiyuan Zhou, Yinghuan Zhang, Li Jiang:
HAWIS: Hardware-Aware Automated WIdth Search for Accurate, Energy-Efficient and Robust Binary Neural Network on ReRAM Dot-Product Engine. ASP-DAC 2022: 226-231 - [c20]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 - [c19]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 - [c18]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 - [c17]Tao Yang, Dongyue Li, Zhuoran Song, Yilong Zhao, Fangxin Liu, Zongwu Wang, Zhezhi He, Li Jiang:
DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture. DATE 2022: 700-705 - [c16]Zongwu Wang, Zhezhi He, Rui Yang, Shiquan Fan, Jie Lin, Fangxin Liu, Yueyang Jia, Chenxi Yuan, Qidong Tang, Li Jiang:
Self-Terminating Write of Multi-Level Cell ReRAM for Efficient Neuromorphic Computing. DATE 2022: 1251-1256 - [c15]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 - [c14]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 - [c13]Yongbiao Chen, Kaicheng Guo, Fangxin Liu, Yusheng Huang, Zhengwei Qi:
Supervised Contrastive Vehicle Quantization for Efficient Vehicle Retrieval. ICMR 2022: 44-48 - [c12]Yongbiao Chen, Sheng Zhang, Fangxin Liu, Zhigang Chang, Mang Ye, Zhengwei Qi:
TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval. ICMR 2022: 127-136 - [c11]Fangxin Liu, Haomin Li, Xiaokang Yang, Li Jiang:
L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks. SIGIR 2022: 1844-1848 - [i5]Yilong Zhao, Li Jiang, Mingyu Gao, Naifeng Jing, Chengyang Gu, Qidong Tang, Fangxin Liu, Tao Yang, Xiaoyao Liang:
RePAST: A ReRAM-based PIM Accelerator for Second-order Training of DNN. CoRR abs/2210.15255 (2022) - 2021
- [j2]Tao Yang, Zhezhi He, Tengchuan Kou, Qingzheng Li, Qi Han, Haibao Yu, Fangxin Liu, Yun Liang, Li Jiang:
BISWSRBS: A Winograd-based CNN Accelerator with a Fine-grained Regular Sparsity Pattern and Mixed Precision Quantization. ACM Trans. Reconfigurable Technol. Syst. 14(4): 18:1-18:28 (2021) - [c10]Tao Yang, Dongyue Li, Yibo Han, Yilong Zhao, Fangxin Liu, Xiaoyao Liang, Zhezhi He, Li Jiang:
PIMGCN: A ReRAM-Based PIM Design for Graph Convolutional Network Acceleration. DAC 2021: 583-588 - [c9]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 - [c8]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 - [c7]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 - [c6]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 - [i4]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) - [i3]Yongbiao Chen, Sheng Zhang, Fangxin Liu, Zhigang Chang, Mang Ye, Zhengwei Qi:
TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval. CoRR abs/2105.01823 (2021) - [i2]Yongbiao Chen, Sheng Zhang, Fangxin Liu, Chenggang Wu, Kaicheng Guo, Zhengwei Qi:
DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification. CoRR abs/2112.04937 (2021) - 2020
- [c5]Jiansong Li, Zihan Jiang, Fangxin Liu, Xiao Dong, Guangli Li, Xueying Wang, Wei Cao, Lei Liu, Yanzhi Wang, Tao Li, Xiaobing Feng:
Characterizing the I/O Pipeline in the Deployment of CNNs on Commercial Accelerators. ISPA/BDCloud/SocialCom/SustainCom 2020: 137-144 - [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)
2010 – 2019
- 2019
- [c4]Fangxin Liu, Kunpeng Xie, Cheng Gong, Shusheng Liu, Ye Lu, Tao Li:
LHC: A Low-Power Heterogeneous Computing Method on Neural Network Accelerator. ICPADS 2019: 326-334 - [c3]Jin Zhang, Xin Wei, Zhen Liu, Fangxin Liu, Tao Li, Tingjuan Lu, Xiaoli Gong:
ExploreBP: A Simulation Tool for Mobile Browser Energy Optimization. SimuTools 2019: 248-257 - 2018
- [c2]Na Wang, Fei Dai, Fangxin Liu, Guomin Zhang:
Dynamic Obstacle Avoidance Planning Algorithm for UAV Based on Dubins Path. ICA3PP (2) 2018: 367-377 - [c1]Na Wang, Nan Di, Fei Dai, Fangxin Liu:
UAV 3D Mobility Model Oriented to Dynamic and Uncertain Environment. ICA3PP (3) 2018: 640-650 - 2017
- [j1]Ming He, Fangxin Liu, Zhuang Miao, Huan Zhou, Qiuli Chen:
A mechanism of topology optimization for underwater acoustic sensor networks based on autonomous underwater vehicles. Int. J. Distributed Sens. Networks 13(1) (2017)
Coauthor Index
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last updated on 2024-12-18 19:20 CET by the dblp team
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