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Published on Feb 13, 2012
Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: NeuFlow: A Runtime Reconfigurable Dataflow Processor for Vision by Yann LeCun (with Clement Farabet)
Yann LeCun is Silver Professor of Computer Science and Neural Science at the Courant Institute of Mathematical Sciences and the Center for Neural Science of New York University. His current interests include machine learning, computer perception and vision, mobile robotics, and computational neuroscience.
In 2010, Clement Farabet started the PhD program at Universite Paris-Est, with Professors Michel Couprie and Laurent Najman, in parallel with his research work at Yale and NYU. His research interests include intelligent hardware, embedded super-computers, computer vision, machine learning, embedded robotics, and more broadly artificial intelligence.
Abstract: We present a scalable hardware architecture to implement general-purpose systems based on convolutional networks. We will first review some of the latest advances in convolutional networks, their applications and the theory behind them, then present our dataflow processor, a highly-optimized architecture for large vector transforms, which represent 99% of the computations in convolutional networks. It was designed with the goal of providing a high-throughput engine for highly-redundant operations, while consuming little power and remaining completely runtime reprogrammable. We present performance comparisons between software versions of our system executing on CPU and GPU machines, and show that our FPGA implementation can outperform these standard computing platforms.