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Demonstration
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 VP & Chief AI Scientist at Facebook and Silver Professor at NYU affiliated with the Courant Institute of Mathematical Sciences & the Center for Data Science. He was the founding Director of Facebook AI Research and of the NYU Center for Data Science. He received an Engineering Diploma from ESIEE (Paris) and a PhD from Sorbonne Université. After a postdoc in Toronto he joined AT&T Bell Labs in 1988, and AT&T Labs in 1996 as Head of Image Processing Research. He joined NYU as a professor in 2003 and Facebook in 2013. His interests include AI machine learning, computer perception, robotics and computational neuroscience. He is the recipient of the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio) for "conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing", a member of the National Academy of Sciences, the National Academy of Engineering and a Chevalier de la Légion d’Honneur.

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