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NeuFlow: a dataflow processor for convolutional nets and other real-time algorithms
Yann LeCun

Tue Dec 07 07:30 PM -- 11:59 PM (PST) @ Georgia A
Event URL: http://js1.cs.nyu.edu:8081/research »

NeuFlow is a new concept of "dataflow" architecture, which is particularly well-suited for algorithms that perform a fixed set of operations on a stream of data (e.g. an image). NeuFlow is particularly efficient for such vision algorithms as Convolutional Networks. The NeuFlow architecture is currently instantiated on an FPGA board built around a Xilinx Virtex-6, which communicate with a laptop computer through a gigabit ethernet connection. It is capable of a sustained performance of 100 billion multiply-accumulate operations per second while consuming less than 15 Watts of power: about 100 times faster than on a conventional processor, and considerably faster than GPUs for a fraction of the power consumtion and a fraction of the volume. The system runs a number of real-time vision demos, such as a face detector, a general object recognition systems (trainable on-line), a pedestrian detector, and a vision system for off-road mobile robots that can classify obstacles from traversable areas. The system can also be trained on-line to recognize just about anything.

Author Information

Yann LeCun (Facebook AI Research and New York University)

Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University. He received the Electrical Engineer Diploma from ESIEE, Paris in 1983, and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty. His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits for computer perception.

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