Publications

Li, Hai, and Brady Taylor. “A Hardware and Software Co-design Framework for Energy Efficient Neuromorphic Systems.” Office of Scientific and Technical Information (OSTI), July 5, 2023. https://doi.org/10.2172/1985762.

Yang, X., H. Yang, J. R. Doppa, P. P. Pande, K. Chakrabartys, and H. Li. “ESSENCE: Exploiting Structured Stochastic Gradient Pruning for Endurance-Aware ReRAM-Based In-Memory Training Systems.” IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems 42, no. 7 (July 1, 2023): 2187–99. https://doi.org/10.1109/TCAD.2022.3216546.

Li, Z., Q. Zheng, Y. Chen, and H. Li. “SpikeSen: Low-Latency In-Sensor-Intelligence Design With Neuromorphic Spiking Neurons.” IEEE Transactions on Circuits and Systems II Express Briefs 70, no. 6 (June 1, 2023): 1876–80. https://doi.org/10.1109/TCSII.2023.3235888.

Zhang, T., D. Cheng, Y. He, Z. Chen, X. Dai, L. Xiong, F. Yan, H. Li, Y. Chen, and W. Wen. “NASRec: Weight Sharing Neural Architecture Search for Recommender Systems.” In ACM Web Conference 2023 Proceedings of the World Wide Web Conference Www 2023, 1199–1207, 2023. https://doi.org/10.1145/3543507.3583446.

Joardar, B. K., J. R. Doppa, H. Li, K. Chakrabarty, and P. P. Pande. “ReaLPrune: ReRAM Crossbar-Aware Lottery Ticket Pruning for CNNs.” IEEE Transactions on Emerging Topics in Computing 11, no. 2 (April 1, 2023): 303–17. https://doi.org/10.1109/TETC.2022.3223630.

Xie, Z., J. Pan, C. C. Chang, J. Hu, and Y. Chen. “The Dark Side: Security and Reliability Concerns in Machine Learning for EDA.” IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems 42, no. 4 (April 1, 2023): 1171–84. https://doi.org/10.1109/TCAD.2022.3199172.

Hanson, E., M. Horton, H. H. Li, and Y. Chen. “DefT: Boosting Scalability of Deformable Convolution Operations on GPUs.” In International Conference on Architectural Support for Programming Languages and Operating Systems ASPLOS, 3:134–46, 2023. https://doi.org/10.1145/3582016.3582017.

Hanson, E., S. Li, X. Qian, H. H. Li, and Y. Chen. “DyNNamic: Dynamically Reshaping, High Data-Reuse Accelerator for Compact DNNs.” IEEE Transactions on Computers 72, no. 3 (March 1, 2023): 880–92. https://doi.org/10.1109/TC.2022.3184272.

Pang, Meng, Binghui Wang, Mang Ye, Yiu-Ming Cheung, Yiran Chen, and Bihan Wen. “DisP+V: A Unified Framework for Disentangling Prototype and Variation From Single Sample per Person.” IEEE Transactions on Neural Networks and Learning Systems 34, no. 2 (February 2023): 867–81. https://doi.org/10.1109/tnnls.2021.3103194.

Augustine, C., and H. Li. “ISLPED 2022: An Experience of a Hybrid Conference in the Time of COVID-19.” IEEE Design and Test 40, no. 1 (February 1, 2023): 105–7. https://doi.org/10.1109/MDAT.2022.3208552.

Chang, C. C., J. Pan, Z. Xie, Y. Li, Y. Lin, J. Hu, and Y. Chen. “Fully Automated Machine Learning Model Development for Analog Placement Quality Prediction.” In Proceedings of the Asia and South Pacific Design Automation Conference ASP DAC, 58–63, 2023. https://doi.org/10.1145/3566097.3567881.

Chang, C. C., J. Pan, Z. Xie, J. Hu, and Y. Chen. “Rethink before Releasing Your Model: ML Model Extraction Attack in EDA.” In Proceedings of the Asia and South Pacific Design Automation Conference ASP DAC, 252–57, 2023. https://doi.org/10.1145/3566097.3567896.

Yang, X., S. Li, Q. Zheng, and Y. Chen. “Improving the Robustness and Efficiency of PIM-Based Architecture by SW/HW Co-Design.” In Proceedings of the Asia and South Pacific Design Automation Conference ASP DAC, 618–23, 2023. https://doi.org/10.1145/3566097.3568358.

Yang, Y., H. Li, and Y. Chen. “Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations.” In Proceedings of the IEEE International Conference on Computer Vision, 16063–74, 2023. https://doi.org/10.1109/ICCV51070.2023.01476.

Song, L., F. Chen, H. Li, and Y. Chen. “Refloat: Low-Cost Floating-Point Processing in ReRAM for Accelerating Iterative Linear Solvers.” In International Conference for High Performance Computing Networking Storage and Analysis Sc, 2023. https://doi.org/10.1145/3581784.3607077.

Kim, B., S. Li, and H. Li. “INCA: Input-stationary Dataflow at Outside-the-box Thinking about Deep Learning Accelerators.” In Proceedings International Symposium on High Performance Computer Architecture, 2023-February:29–41, 2023. https://doi.org/10.1109/HPCA56546.2023.10070992.

Zhang, Q., S. Li, G. Zhou, J. Pan, C. C. Chang, Y. Chen, and Z. Xie. “PANDA: Architecture-Level Power Evaluation by Unifying Analytical and Machine Learning Solutions.” In IEEE ACM International Conference on Computer Aided Design Digest of Technical Papers Iccad, 2023. https://doi.org/10.1109/ICCAD57390.2023.10323665.

Zhang, J., A. Muhamed, A. Anantharaman, G. Wang, C. Chen, K. Zhong, Q. Cui, et al. “ReAugKD: Retrieval-Augmented Knowledge Distillation For Pre-trained Language Models.” In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2:1128–36, 2023. https://doi.org/10.18653/v1/2023.acl-short.97.

Wu, C., X. Yang, Y. Chen, and M. Li. “Photonic Bayesian Neural Network Using Programmed Optical Noises.” IEEE Journal of Selected Topics in Quantum Electronics 29, no. 2 (January 1, 2023). https://doi.org/10.1109/JSTQE.2022.3217819.

Zhang, J., N. Inkawhich, R. Linderman, Y. Chen, and H. Li. “Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments.” In Proceedings 2023 IEEE Winter Conference on Applications of Computer Vision Wacv 2023, 5520–29, 2023. https://doi.org/10.1109/WACV56688.2023.00549.

Kim, B., Z. Du, J. Sun, and Y. Chen. “Invited Paper: Towards the Efficiency, Heterogeneity, and Robustness of Edge AI.” In IEEE ACM International Conference on Computer Aided Design Digest of Technical Papers Iccad, 2023. https://doi.org/10.1109/ICCAD57390.2023.10323922.

Petri, R., G. L. Zhang, Y. Chen, U. Schlichtmann, and B. Li. “PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration.” In Proceedings Design Automation Conference, Vol. 2023-July, 2023. https://doi.org/10.1109/DAC56929.2023.10247868.

Su, Q., A. Netchaev, H. Li, and S. Ji. “FLSL: Feature-level Self-supervised Learning.” In Advances in Neural Information Processing Systems, Vol. 36, 2023.

Hu, S., Y. Chen, Q. Zhu, and A. W. Colombo. “Guest Editorial Machine Learning for Resilient Industrial Cyber-Physical Systems.” IEEE Transactions on Automation Science and Engineering 20, no. 1 (January 1, 2023): 3–4. https://doi.org/10.1109/TASE.2022.3223583.

Tung, C. H., B. K. Joardar, P. P. Pande, J. R. Doppa, H. H. Li, and K. Chakrabarty. “Dynamic Task Remapping for Reliable CNN Training on ReRAM Crossbars.” In Proceedings Design Automation and Test in Europe Date, Vol. 2023-April, 2023. https://doi.org/10.23919/DATE56975.2023.10137238.