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2020 – today
- 2024
[j3]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)- [c21]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 - [c20]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 - [c19]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 - [c18]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 - [c17]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 - [c16]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 - 2023
- [j2]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) - [c15]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
- [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) - [c14]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 - [c13]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 - [c12]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 - [c11]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 - [c10]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 - [c9]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 - [c8]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 - [c7]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 - [c6]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 - [c5]Chen Nie, Zongwu Wang, Qidong Tang, Chenyang Lv, Li Jiang, Zhezhi He:
Cross-layer Designs against Non-ideal Effects in ReRAM-based Processing-in-Memory System. ISQED 2022: 1-6 - 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 - [i1]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)
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