Low cost GPU based neural network simulates the brain

http://stanford.edu/~acoates/papers/CoatesHuvalWangWuNgCatanzaro_icml2013.pdf

In a new paper, Stanford’s Andrew Ng describes how to use graphics microprocessors to build a $20,000 computerized brain that is similar to the cat-detector he developed with Google last year for $1M.

To test his hypothesis about GPU-driven Deep Learning, he also built a larger version of the platform for $100,000.  It utilized 64 Nvidia GTX 680 GPUs on 16 computers. It was able to accomplish the same cat-spotting tasks as the Google system, which needed 1,000 computers to operate.  That system, modeling the activities and processes of the human brain, was able to learn what a cat looks like, and then translate that knowledge into spotting different cats across multiple YouTube videos.

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