 | Up a level |
Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
Use the mouse wheel or scroll gestures to zoom into the graph.
You can click on the nodes and links to highlight them and move the nodes by dragging them.
Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Fang, C., Shen, Z., Zhao, S., Wang, C., Tian, F., Yang, J., & Sawan, M. (2024, April). A 0.078 pJ/SOP Unstructured Sparsity-Aware Spiking Attention/Convolution Processor with 3D Compute Array [Paper]. IEEE Custom Integrated Circuits Conference (CICC 2024), Denver, CO, USA. External link
Fang, C., Shen, Z., Wang, Z., Wang, C., Zhao, S., Tian, F., Yang, J., & Sawan, M. (2024). An Energy-Efficient Unstructured Sparsity-Aware Deep SNN Accelerator With 3-D Computation Array. IEEE Journal of Solid-State Circuits, 1-13. External link
Fang, C., Wang, C., Zhao, S., Tian, F., Yang, J., & Sawan, M. (2023). A 510 μW 0.738-mm² 6.2-pJ/SOP Online Learning Multi-Topology SNN Processor With Unified Computation Engine in 40-nm CMOS. IEEE Transactions on Biomedical Circuits and Systems, 17(3), 507-520. External link
Li, S., Wu, D., Zhao, S., Yang, J., & Sawan, M. (2025). Memory-Efficient Intrinsic Gating Adaptation for Enhanced On-Device Epilepsy Diagnosis. IEEE Journal of Biomedical and Health Informatics, PP, 14 pages. External link
Li, H., Wang, J., Zhao, S., Tian, F., Yang, J., & Sawan, M. (2022, June). Real-time Biosignal Recording and Machine-Learning Analysis System [Paper]. 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS 2022), Incheon, Republic of Korea. External link
Tian, F., Yang, J., Zhao, S., & Sawan, M. (2023). NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications. Frontiers in Neuroscience, 17, 1093865 (14 pages). External link
Tian, F., Zhao, S., He, J., Chen, J., Wang, X., Yang, J., Sawan, M., Tsui, C.-Y., & Cheng, K.-T. T. (2023, October). NOLS: A Near-sensor On-chip Learning System with Direct Feedback Alignment for Personalized Wearable Heart Health Monitoring [Paper]. Biomedical Circuits and Systems Conference (BioCAS 2023), Toronto, ON, Canada (5 pages). External link
Tian, F., Yang, J., Zhao, S., & Sawan, M. (2021, May). A New Neuromorphic Computing Approach for Epileptic Seizure Prediction [Paper]. International Symposium on Circuits and Systems (ISCAS 2021), Daegu, Korea (5 pages). External link
Wu, D., Zhao, S., Yang, J., & Sawan, M. (2023). Software-Hardware Co-Design for Energy-Efficient Continuous Health Monitoring via Task-Aware Compression. IEEE Transactions on Biomedical Circuits and Systems, 17(2), 180-191. External link
Wang, J., Zhao, S., Yang, J., & Sawan, M. (2023, October). An Event-driven Neural Signal Processor for Closed-loop Seizure Prediction [Paper]. Biomedical Circuits and Systems Conference (BioCAS 2023), Toronto, ON, Canada (5 pages). External link
Wang, J., Zhao, S., Fang, C., Yang, J., & Sawan, M. (2023, June). Live Demonstration: Real-time Analyses of Biosignals based on a Dedicated CMOS Configurable Deep Learning Engine [Paper]. 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS 2023), Hangzhou, China (2 pages). External link
Xu, Y., Yang, J., Zhao, S., Wu, H., & Sawan, M. (2020, August). An End-to-End Deep Learning Approach for Epileptic Seizure Prediction [Paper]. 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2020), Genova, Italy. External link
Yang, J., Zhao, S., Lin, S., Hou, Q., Wang, J., & Sawan, M. (2024). Precise and low-power closed-loop neuromodulation through algorithm-integrated circuit co-design. Frontiers in Neuroscience, 18, 13 pages. Available
Zhao, S., Wang, Z., Sun, H., Wang, S., Chen, Z., Fu, C., Yang, J., & Sawan, M. (2026). HAQ-ViT: A hardware-aware post-training quantization for efficient vision transformer inference. Knowledge-Based Systems, 342, 115830 (14 pages). External link
Zhao, S., Wang, S., Wang, Z., Fang, C., Tian, F., Chen, J., Fu, C., Yang, J., & Sawan, M. (2025). BoostViT: Booth-Serial Skipping and Tunable Scaling for Vision Transformers. IEEE Transactions on Circuits and Systems I Regular Papers, 12 pages. External link
Zhao, S., Li, S., Wu, D., Tian, F., Yang, J., & Sawan, M. (2025). A Resource-Efficient Algorithm-Hardware Co-Design Towards Semi-Supervised Neurological Symptoms Prediction. IEEE Transactions on Instrumentation and Measurement, 74, 12 pages. External link
Zhao, S., Wang, C., Fang, C., Tian, F., Yang, J., & Sawan, M. (2024). HybMED: A Hybrid Neural Network Training Processor with Multi-Sparsity Exploitation for Internet of Medical Things. IEEE Transactions on Biomedical Circuits and Systems, 18(5), 1178-1189. External link
Zhao, S., Yang, J., Wang, J., Fang, C., Liu, T., Zhang, S., & Sawan, M. (2023). A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses. IEEE Transactions on Biomedical Circuits and Systems, 17(3), 598-609. External link
Zhao, S., Wu, D., Yang, J., & Sawan, M. (2022, October). A Resource-Efficient and Data-Restricted Training Method Towards Neurological Symptoms Prediction [Paper]. Biomedical Circuits and Systems Conference (BioCAS 2022), Taipei, Taiwan. External link
Zhao, S., Fang, C., Yang, J., & Sawan, M. (2022). Emerging Energy-Efficient Biosignal-Dedicated Circuit Techniques: A Tutorial Brief. IEEE Transactions on Circuits & Systems II Express Briefs, 69(6), 2592-2597. External link
Zhao, S., Yang, J., & Sawan, M. (2022). Energy-Efficient Neural Network for Epileptic Seizure Prediction. IEEE Transactions on Biomedical Engineering, 69(1), 401-411. External link
Zhang, Y., Savaria, Y., Zhao, S., Torcato Mordido, G. F., Sawan, M., & Leduc-Primeau, F. (2022, July). Tiny CNN for Seizure Prediction in Wearable Biomedical Devices [Paper]. 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2022), Glasgow, United Kingdom. External link
Zhao, S., Yang, J., Xu, Y., & Sawan, M. (2020, October). Binary Single-Dimensional Convolutional Neural Network for Seizure Prediction [Paper]. International Symposium on Circuits and Systems (ISCAS 2020), Seville, Spain (5 pages). External link
