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Oral
Band-limited Training and Inference for Convolutional Neural Networks
Adam Dziedzic · John Paparrizos · Sanjay Krishnan · Aaron Elmore · Michael Franklin

Wed Jun 12 02:20 PM -- 02:25 PM (PDT) @ Seaside Ballroom
in Applications: Computer Vision »

The convolutional layers are core building blocks of neural network architecture. In general, a convolutional filter applies to the entire frequency spectrum of an input signal. We explore artificially constraining the frequency spectra of these filters, called band-limiting, during Convolutional Neural Networks (CNN) training. The band-limiting applies to both the feedforward and backpropagation steps. Through an extensive evaluation over time-series and image datasets, we observe that CNNs are resilient to this compression scheme and results suggest that CNNs learn to leverage lower-frequency components. An extensive experimental evaluation across 1D and 2D CNN training tasks illustrates: (1) band-limited training can effectively control the resource usage (GPU and memory); (2) models trained with band-limited layers retain high prediction accuracy; and (3) requires no modification to existing training algorithms or neural network architecture to use unlike other compression schemes.

[ Slides] 

Author Information

Adam Dziedzic (University of Chicago)

I'm a PhD student at the University of Chicago, advised by Prof. Sanjay Krishnan. I'm passionate about empowering users with data driven decisions. Currently, I work on the Band-Limited convolutional neural networks (ConvNets) and the DeepLens project. I was an intern at Microsoft Research and worked on recommendation of hybrid physical designs (B+ tree and Columnstores) for SQL Server. Currently, I work on the intersection of databases and machine/deep learning. I obtained my Bachelor's and Master's degrees from Warsaw University of Technology in Poland. I was also studying at DTU (Technical University of Denmark) and carried out research on databases in the DIAS group at EPFL, Switzerland. Previously, I had internships at CERN (Geneva, Switzerland), Barclays Investment Bank in London (UK), Microsoft Research (Redmond, USA) and Google (Madison, USA). I spend the rest of my waking hours on reading books, taking MOOC courses, especially on Machine & Deep Learning, enjoying many sports, playing a guitar and practicing Aikido.

John Paparrizos (University of Chicago)
Sanjay Krishnan (U Chicago)
Aaron Elmore (University of Chicago)
Michael Franklin (University of Chicago)

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