About

I am a 2nd-year PhD student from Department of Computer Science and Engineering, University of Notre Dame. My research interest includes computing-in-momory (CIM), non-volatile AI accelerator, and AI computing for medicial purpose. Now I am very fortunate to work with Prof. Yiyu Shi and Prof. X. Sharon Hu, University of Notre Dame. Before my PhD study, I received BS and MS degree from Huazhong University of Science and Technology.

You can find my CV here.

Feel free to contact me if you have any idea or project to share, email: yqin3 [at] nd [dot] edu.

News

• 🎉️Our paper is accepted by ICCAD 2024 ! TSB: Tiny Shared Block for Efficient DNN Deployment on NVCIM Accelerators
• 🎉️Our paper is accepted by ICCAD 2024 ! Towards Uncertainty-Quantifiable Biomedical Intelligence: Mixed-signal Compute-in-Entropy for Bayesian Neural Networks
• 🎉️I give a talk on ACCESS summer school !
• 🎉️Our poster is on ACCESS Techonlogy Symposium !
• 🎉️Our paper is selected as the Best Paper in ICCAD 2023 ! Improving realistic worst-case performance of NVCiM DNN accelerators through training with right-censored gaussian noise
• 🎉️Our paper is on arxiv ! Negative Feedback Training: A Novel Concept to Improve Robustness of NVCIM DNN Accelerators
• 🎉️Our paper is accepted by Advanced Intelligent Systems and selected as Back Cover ! Recent progress on memristive convolutional neural networks for edge intelligence
• 🎉️Our paper is accepted by IEEE Transactions on Electron Devices ! Design of high robustness BNN inference accelerator based on binary memristors

Publications

Journal

1. Han Bao, Yifan Qin, Jia Chen, Ling Yang, Jiancong Li, Houji Zhou, Yi Li, and Xiangshui Miao. “Quantization and sparsity-aware processing for energy-efficient NVM-based convolutional neural networks”. In:Frontiers in Electronics 3 (2022), p. 954661.
2. Yifan Qin, Han Bao, Feng Wang, Jia Chen, Yi Li, and Xiangshui Miao. “Recent progress on memristive convo- lutional neural networks for edge intelligence”. In:Advanced Intelligent Systems 2.11 (2020), p. 2000114. (Back Cover).
3. Yifan Qin, Rui Kuang, Xiaodi Huang, Yi Li, Jia Chen, and Xiangshui Miao. “Design of high robustness BNN inference accelerator based on binary memristors”. In: IEEE Transactions on Electron Devices 67.8 (2020), pp. 3435–3441.

Conference

1. Likai Pei*, Yifan Qin*, Zephan M. Enciso, Boyang Cheng, Jianbo Liu, Steven Davis, Zhenge Jia, Michael Niemier, Yiyu Shi, X. Sharon Hu and Ningyuan Cao. “Towards Uncertainty-Quantifiable Biomedical Intelligence: Mixed-signal Compute-in-Entropy for Bayesian Neural Networks”. (*Equal contribution) (Accepted by ICCAD 2024)

2. Yifan Qin, Zheyu Yan, Zixuan Pan, Wujie Wen, Xiaobo Sharon Hu, and Yiyu Shi. “TSB: Tiny Shared Block for Efficient DNN Deployment on NVCIM Accelerators”. In: arXiv preprint arXiv:2406.06544 (2024). (Accepted by ICCAD 2024)

3. Yifan Qin, Zheyu Yan, Wujie Wen, Xiaobo Sharon Hu, and Yiyu Shi. “Negative Feedback Training: A Novel Concept to Improve Robustness of NVCiM DNN Accelerators”. In: arXiv preprint arXiv:2305.14561 (2023). (Under review)

4. Zheyu Yan, Yifan Qin, Xiaobo Sharon Hu, and Yiyu Shi. “On the viability of using LLMs for SW/HW co-design: An example in designing CiM DNN accelerators”. In: 2023 IEEE 36th International System-on-Chip Conference (SOCC). IEEE. 2023, pp. 1–6.

5. Zheyu Yan, Yifan Qin, Wujie Wen, Xiaobo Sharon Hu, and Yiyu Shi. “Improving realistic worst-case perfor- mance of NVCiM DNN accelerators through training with right-censored gaussian noise”. In: 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD). IEEE. 2023, pp. 1–9. (Best Paper)(2 out of 750 submissions).

Chip Demo

• 06/2024 Life-Threatening Ventricular Arrhythmia Detection Soft/Hard-ware Co-Design Accelerator (AC-Codesign V1) ——Access center, HK

High efficiency CNN accelerator for VA detection demo, full stack design from UI, front-end to back-end. For each patient, the accelerator processes 6 files (512 data points/file, 250Hz sample rate) and vote for the final diagnosis.