Title Revolutionizing Edge AI: Improved and Automated Model Compression Bio Kaiqi Zhao is a final-year Ph.D. candidate advised by Prof. Ming Zhao in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU). Her research interests are machine learning (ML) and deep learning (DL) model compression as well as cloud and edge computing. Central to her research are innovating solutions to optimize large-scale ML/DL models for Internet-of-Things (IoT) data-driven applications. She has published first-authored papers at top-tier AI conferences, e.g., AISTATS (2023 and 2024), ICASSP (Oral), and InterSpeech (Oral), and top-tier edge computing conferences, e.g., SEC, and won the Best Poster Award at SEC'24. She did three research internships at Amazon Web Services as an Applied Scientist. One of her internship works about compressing speech recognition models has been integrated into the Amazon Alex library for production usage. Additionally, she was awarded the Graduate College Completion Fellowship, the most prestigious award for graduate students at ASU, and SCAI Doctoral Fellowships in 2022 and 2023. Abstract Machine learning models are increasingly employed by smart devices on the edge to support important applications such as real-time virtual assistants and privacy-preserving healthcare. However, deploying state-of-the-art (SOTA) deep learning models on devices faces multiple serious challenges. First, it is infeasible to deploy large models on resource-constrained edge devices whereas small models cannot achieve the SOTA accuracy. Second, it is difficult to customize the models according to diverse application requirements in accuracy and speed and diverse capabilities of edge devices. This talk presents several novel solutions to comprehensively address the above challenges through automated and improved model compression. First, it introduces Automatic Attention Pruning (AAP), an adaptive, attention-based pruning approach to automatically reduce model parameters while meeting diverse user objectives in model size, speed, and accuracy. AAP achieves an impressive 92.72% parameter reduction in ResNet-101 on Tiny-ImageNet without causing any accuracy loss. Second, it presents Self-Supervised Quantization-Aware Knowledge Distillation, a framework for reducing model precision without supervision from labeled training data. For example, it quantizes VGG-8 to 2 bits on CIFAR-10 without any accuracy loss. Finally, the talk explores two more works, Contrastive Knowledge Distillation and Module Replacing, for further improving the performance of small models. All the works presented in this talk are designed to address real-world challenges, with applications extending to production environments such as Amazon Alexa, and have been successfully deployed on diverse hardware platforms, including cloud instances and edge devices, catalyzing AI for the edge. ________________________________________________________________________________ Microsoft Teams meeting Join on your computer, mobile app or room device Click here to join the meeting< https://teams.microsoft.com/l/meetup-join/19%3ameeting_MzIxZWVkM2MtMjg1MC00MjQ5LWIwY2MtZWM5ZmY2ZjdlNmY4%40thread.v2/0?context=%7b%22Tid%22%3a%22f3308007-477c-4a70-8889-34611817c55a%22%2c%22Oid%22%3a%22685c3f4f-29d5-4141-ada5-0fdeab8480e4%22%7d > Meeting ID: 283 234 327 430 Passcode: Sf3wbd Download Teams< https://www.microsoft.com/en-us/microsoft-teams/download-app > | Join on the web< https://www.microsoft.com/microsoft-teams/join-a-meeting > Or call in (audio only) +1 614-706-6572,,80448131#<tel:+16147066572,,80448131#> United States, Columbus Phone Conference ID: 804 481 31# Find a local number< https://dialin.teams.microsoft.com/8f5f7319-0053-4423-a154-4f8b6e7fb7dd?id=80448131 > | Reset PIN< https://dialin.teams.microsoft.com/usp/pstnconferencing > [ https://www.ohio.edu/sites/default/files/2018-11/invite_logo_teams.jpg ] If you encounter issues with this meeting, please visit the Help link. 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