Abstract: The rapid deployment of machine learning system has witnessed various challenges such as high computation and privacy/security concerns. In this talk, we will first discuss the current challenges and advances in efficient machine learning. We will present several machine learning accelerations through algorithm-hardware codesign, on various computing platforms such as GPU, MCU, and ReRAM. On the other hand, Machine-Learning-As-A-Service (MLaaS) provides cloud-based tools to mitigate the cost and risk of building individual ML platforms. Privacy-preserving machine learning (PPML) serves as a good solution to protect sensitive user data. However, the introduced crypto-primitives come at extra high computation and communication overhead and potentially prohibit the machine learning popularity. We will present a systematic acceleration framework that enables low latency, high energy efficiency & accuracy, and security-guaranteed machine learning.
Bio: Caiwen Ding is an assistant professor in the Department of Computer Science & Engineering at the University of Connecticut (UConn). He received his Ph.D. degree from Northeastern University, Boston in 2019, supervised by Prof. Yanzhi Wang. His research interests mainly include efficient machine learning system, privacy preserving machine learning. His work has been published in high-impact venues (e.g., DAC, ICCAD, ASPLOS, ISCA, MICRO, HPCA, SC, FPGA, Oakland, CCS, MLSys, CVPR, IJCAI, AAAI, ACL, ICML, EMNLP, ICRA, DATE, IEEE TCAD, IEEE TPDS). He has received the best paper nomination at DATE 2018 and DATE 2021, best paper award at the DL Hardware Co-Design for AI Acceleration (DCAA) workshop at AAAI 2023, publicity paper at DAC 2022, and the 2021 Excellent in Teaching Award from UConn Provost. His team won first place in accuracy and fourth place overall at the 2022 TinyML Design Contest at ICCAD. His research has been funded by NSF, DOE, USDA, SRC, and multiple industry
sponsors such as Travelers, Eversource.