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Keynote: Design of Efficient and Privacy Preserving Machine Learning
DescriptionThe 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 and accuracy, and security-guaranteed machine learning.
Presenter
Event Type
Workshop
TimeSunday, 12 November 20232:05pm - 3pm MST
Location704-706
Accelerators
Codesign
Heterogeneous Computing
Task Parallelism
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