Publications

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.

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.

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.

Linderman, R., J. Zhang, N. Inkawhich, H. Li, and Y. Chen. “FINE-GRAIN INFERENCE ON OUT-OF-DISTRIBUTION DATA WITH HIERARCHICAL CLASSIFICATION.” In Proceedings of Machine Learning Research, 232:162–83, 2023.

Zhang, J., A. Li, M. Tang, J. Sun, X. Chen, F. Zhang, C. Chen, Y. Chen, and H. Li. “Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction.” In Proceedings of Machine Learning Research, 202:41354–81, 2023.

Qiao, X., and H. Li. “On a New Type of Neural Computation for Probabilistic Symbolic Reasoning.” In Proceedings of the International Joint Conference on Neural Networks, Vol. 2023-June, 2023. https://doi.org/10.1109/IJCNN54540.2023.10191306.

Zhang, L., Y. Chen, A. Li, B. Wang, F. Li, J. Cao, and B. Niu. “Interpreting Disparate Privacy-Utility Tradeoff in Adversarial Learning via Attribute Correlation.” In Proceedings 2023 IEEE Winter Conference on Applications of Computer Vision Wacv 2023, 4690–98, 2023. https://doi.org/10.1109/WACV56688.2023.00468.

Wolters, C., B. Taylor, E. Hanson, X. Yang, U. Schlichtmann, and Y. Chen. “Biologically Plausible Learning on Neuromorphic Hardware Architectures.” In Midwest Symposium on Circuits and Systems, 733–37, 2023. https://doi.org/10.1109/MWSCAS57524.2023.10405905.

Zheng, Q., S. Li, Y. Wang, Z. Li, Y. Chen, and H. H. Li. “Accelerating Sparse Attention with a Reconfigurable Non-volatile Processing-In-Memory Architecture.” In Proceedings Design Automation Conference, Vol. 2023-July, 2023. https://doi.org/10.1109/DAC56929.2023.10247908.

Xie, Z., J. Pan, C. C. Chang, R. Liang, E. C. Barboza, and Y. Chen. “Deep Learning for Routability.” In Machine Learning Applications in Electronic Design Automation, 35–61, 2023. https://doi.org/10.1007/978-3-031-13074-8_2.

Sun, J., Z. Xu, D. Yang, V. Nath, W. Li, C. Zhao, D. Xu, Y. Chen, and H. R. Roth. “Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples.” In Proceedings of the IEEE International Conference on Computer Vision, 5180–89, 2023. https://doi.org/10.1109/ICCV51070.2023.00480.

Li, H. H. “MWSCAS Guest Editorial Special Issue Based on the 64th International Midwest Symposium on Circuits and Systems.” IEEE Transactions on Circuits and Systems I Regular Papers 70, no. 1 (January 1, 2023): 1–2. https://doi.org/10.1109/TCSI.2022.3226647.

Zhang, J., H. Wang, G. S. Ravi, F. T. Chong, S. Han, F. Mueller, and Y. Chen. “DISQ: Dynamic Iteration Skipping for Variational Quantum Algorithms.” In Proceedings 2023 IEEE International Conference on Quantum Computing and Engineering Qce 2023, 1:1062–73, 2023. https://doi.org/10.1109/QCE57702.2023.00120.

Shafique, M., T. Theocharides, H. Li, and C. Jason Xue. “Introduction to the Special Issue on Accelerating AI on the Edge - Part 2.” ACM Transactions on Embedded Computing Systems 21, no. 6 (December 12, 2022). https://doi.org/10.1145/3563127.

Du, Z., J. Sun, A. Li, P. Y. Chen, J. Zhang, H. Li, and Y. Chen. “Rethinking normalization methods in federated learning.” In Distributedml 2022 Proceedings of the 3rd International Workshop on Distributed Machine Learning Part of Conext 2022, 16–22, 2022. https://doi.org/10.1145/3565010.3569062.

Liu, F., W. Zhao, Z. Wang, Y. Zhao, T. Yang, Y. Chen, and L. Jiang. “IVQ: In-Memory Acceleration of DNN Inference Exploiting Varied Quantization.” IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems 41, no. 12 (December 1, 2022): 5313–26. https://doi.org/10.1109/TCAD.2022.3156017.

Zheng, Q., X. Li, Y. Guan, Z. Wang, Y. Cai, Y. Chen, G. Sun, and R. Huang. “PIMulator-NN: An Event-Driven, Cross-Level Simulation Framework for Processing-In-Memory-Based Neural Network Accelerators.” IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems 41, no. 12 (December 1, 2022): 5464–75. https://doi.org/10.1109/TCAD.2022.3160947.

Li, H. H. “Guest Editorial Special Issue on the International Symposium on Integrated Circuits and Systems - ISICAS 2022.” IEEE Transactions on Circuits and Systems I Regular Papers 69, no. 12 (December 1, 2022): 4730. https://doi.org/10.1109/TCSI.2022.3219319.

Li, T., N. Jing, J. Jiang, Q. Wang, Z. Mao, and Y. Chen. “A Novel Architecture Design for Output Significance Aligned Flow with Adaptive Control in ReRAM-based Neural Network Accelerator.” ACM Transactions on Design Automation of Electronic Systems 27, no. 6 (November 22, 2022). https://doi.org/10.1145/3510819.

Hu, S., S. Yu, H. Li, and V. Piuri. “Guest Editorial Special Issue on Security, Privacy, and Trustworthiness in Intelligent Cyber-Physical Systems and Internet of Things.” IEEE Internet of Things Journal 9, no. 22 (November 15, 2022): 22044–47. https://doi.org/10.1109/JIOT.2022.3207335.

Chen, X., D. Li, Y. Chen, and J. Xiong. “Boosting the sensing granularity of acoustic signals by exploiting hardware non-linearity.” In Hotnets 2022 Proceedings of the 2022 21st ACM Workshop on Hot Topics in Networks, 53–59, 2022. https://doi.org/10.1145/3563766.3564091.

Sun, J., A. Li, L. Duan, S. Alam, X. Deng, X. Guo, H. Wang, et al. “FedSEA: A Semi-Asynchronous Federated Learning Framework for Extremely Heterogeneous Devices.” In Sensys 2022 Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems, 106–19, 2022. https://doi.org/10.1145/3560905.3568538.

Ogbogu, C., A. I. Arka, B. K. Joardar, J. R. Doppa, H. Li, K. Chakrabarty, and P. P. Pande. “Accelerating Large-Scale Graph Neural Network Training on Crossbar Diet.” IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems 41, no. 11 (November 1, 2022): 3626–37. https://doi.org/10.1109/TCAD.2022.3197342.