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DTSTART;TZID=America/Denver:20231112T140500
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UID:submissions.supercomputing.org_SC23_sess437_misc288@linklings.com
SUMMARY:Keynote: Design of Efficient and Privacy Preserving Machine Learni
 ng
DESCRIPTION:Caiwen Ding (University of Connecticut)\n\nThe rapid deploymen
 t of machine learning system has witnessed various challenges such as high
  computation and privacy/security concerns. In this talk, we will first di
 scuss the current challenges and advances in efficient machine learning. W
 e will present several machine learning accelerations through algorithm-ha
 rdware codesign, on various computing platforms such as GPU, MCU, and ReRA
 M. On the other hand, Machine-Learning-As-A-Service (MLaaS) provides cloud
 -based tools to mitigate the cost and risk of building individual ML platf
 orms. 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 ac
 celeration framework that enables low latency, high energy efficiency and 
 accuracy, and security-guaranteed machine learning.\n\nTag: Accelerators, 
 Codesign, Heterogeneous Computing, Task Parallelism\n\nRegistration Catego
 ry: Workshop Reg Pass\n\nSession Chairs: Tong Shu (University of North Tex
 as), Seung-Hwan Lim (Oak Ridge National Laboratory (ORNL)), Pavan Balaji (
 Meta AI), and Sanjukta Bhowmick (University of North Texas)\n\n
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