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Coded-InvNet for Resilient Prediction Serving Systems
Tuan Dinh · Kangwook Lee

Thu Jul 22 09:00 AM -- 11:00 AM (PDT) @

Inspired by a new coded computation algorithm for invertible functions, we propose Coded-InvNet a new approach to design resilient prediction serving systems that can gracefully handle stragglers or node failures. Coded-InvNet leverages recent findings in the deep learning literature such as invertible neural networks, Manifold Mixup, and domain translation algorithms, identifying interesting research directions that span across machine learning and systems. Our experimental results show that Coded-InvNet can outperform existing approaches, especially when the compute resource overhead is as low as 10%. For instance, without knowing which of the ten workers is going to fail, our algorithm can design a backup task so that it can correctly recover the missing prediction result with an accuracy of 85.9%, significantly outperforming the previous SOTA by 32.5%.

Author Information

Tuan Dinh (University of Wisconsin-Madison)
Kangwook Lee (UW Madison)

I am an Assistant Professor at the Electrical and Computer Engineering department and the Computer Sciences department (by courtesy) at the University of Wisconsin-Madison. Previously, I was a Research Assistant Professor at Information and Electronics Research Institute of KAIST, working with Prof. Changho Suh. Before that, I was a postdoctoral scholar at the same institute. I received my PhD in May 2016 from the Electrical Engineering and Computer Science department at UC Berkeley and my Master of Science degree from the same department in December 2012, both under the supervision of Prof. Kannan Ramchandran. I was a member of Berkeley Laboratory of Information and System Sciences (BLISS, aka Wireless Foundation) and BASiCS Group. I received my Bachelor of Science degree in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST) in May 2010.

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