*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
To join the meeting on a computer or mobile phone: https://bluejeans.com/505935226
Phone Dial-in
+1.888.748.9073 (United States(Primary))
+1.844.540.8065 (United States(Primary))
+1.408.419.1715 (United States(San Jose))
+1.408.915.6290 (United States(San Jose))
(Global Numbers)
Meeting ID: 505 935 226
Room System
199.48.152.152 or bjn.vc
Meeting ID: 505 935 226
Want to test your video connection?
Talk Title: An Integrated Approach for Efficient Neural Network Design, Training, and Inference
Talk Abstract: One of the main challenges in designing, training, and implementing Neural Networks is their high demand for computational and memory resources. Designing a model for a new task requires searching through an exponentially large space to find the right architecture, which requires multiple training runs on a large dataset. This has a prohibitive computational cost, as training each candidate architecture often requires millions of iterations.
Even after the right architecture with good accuracy is found, implementing it on a target hardware platform to meet latency and power constraints is not straightforward.
I will present a framework that efficiently utilizes reduced-precision computing to address the above challenges by considering the full stack of designing, training, and implementing the model on a target platform. This is achieved through careful analysis of the numerical instabilities associated with reduced-precision matrix operations, incorporation of a novel second-order, mixed-precision quantization approach, and a framework for hardware aware neural network design.
Bio: Amir Gholami is a postdoctoral research fellow in BAIR Lab at UC Berkeley. He received his PhD in Computational Science and Engineering Mathematics from UT Austin, working with Prof. George Biros on bio-physics based image analysis, a research topic which received UT Austin’s best doctoral dissertation award in 2018. Amir has extensive experience in High Performance Computing, second-order optimization methods, image registration, and large scale inverse problems, developing codes that have been scaled up to 200K cores. He is a Melosh Medal finalist, recipient of best student paper award in SC'17, Gold Medal in the ACM Student Research Competition in 2015, as well as best student paper finalist in SC’14.
