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

F. Liu, W. Zhao, Z. Wang, T. Yang, и L. Jiang. ACM Great Lakes Symposium on VLSI, стр. 253-258. ACM, (2021)

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Wang Wang

Linfang Wang

Investigation of the functions of 53BP1 in DNA demethylationL. Wang. Uni Marburg, (2009)

Paul Wang

Lien Wang

Scheifoo Wang

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SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network.F. Liu, W. Zhao, Z. He, Z. Wang, Y. Zhao, T. Yang, J. Feng, X. Liang, и L. Jiang. ICCD, стр. 417-424. IEEE, (2021)Cross-layer Designs against Non-ideal Effects in ReRAM-based Processing-in-Memory System.C. Nie, Z. Wang, Q. Tang, C. Lv, L. Jiang, и Z. He. ISQED, стр. 1-6. IEEE, (2022)SPARK: Scalable and Precision-Aware Acceleration of Neural Networks via Efficient Encoding.F. Liu, N. Yang, H. Li, Z. Wang, Z. Song, S. Pei, и L. Jiang. HPCA, стр. 1029-1042. IEEE, (2024)Improving Neural Network Efficiency via Post-training Quantization with Adaptive Floating-Point.F. Liu, W. Zhao, Z. He, Y. Wang, Z. Wang, C. Dai, X. Liang, и L. Jiang. ICCV, стр. 5261-5270. IEEE, (2021)DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture.T. Yang, D. Li, Z. Song, Y. Zhao, F. Liu, Z. Wang, Z. He, и L. Jiang. DATE, стр. 700-705. IEEE, (2022)HAWIS: Hardware-Aware Automated WIdth Search for Accurate, Energy-Efficient and Robust Binary Neural Network on ReRAM Dot-Product Engine.Q. Tang, Z. He, F. Liu, Z. Wang, Y. Zhou, Y. Zhang, и L. Jiang. ASP-DAC, стр. 226-231. IEEE, (2022)SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks.F. Liu, W. Zhao, Y. Chen, Z. Wang, и L. Jiang. AAAI, стр. 1692-1701. AAAI Press, (2022)Bit-Transformer: Transforming Bit-level Sparsity into Higher Preformance in ReRAM-based Accelerator.F. Liu, W. Zhao, Z. He, Z. Wang, Y. Zhao, Y. Chen, и L. Jiang. ICCAD, стр. 1-9. IEEE, (2021)Randomize and Match: Exploiting Irregular Sparsity for Energy Efficient Processing in SNNs.F. Liu, Z. Wang, W. Zhao, Y. Chen, T. Yang, X. Yang, и L. Jiang. ICCD, стр. 451-454. IEEE, (2022)DynSNN: A Dynamic Approach to Reduce Redundancy in Spiking Neural Networks.F. Liu, W. Zhao, Y. Chen, Z. Wang, и F. Dai. ICASSP, стр. 2130-2134. IEEE, (2022)

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