This talk attempts to debunk the GPU vs. CPU myth in deep neural networks applications, specifically for the connectomics problem in neuroscience. The talk will discuss how to performance engineer computer vision CNN networks (such as LeNet) with Caffe to achieve inference throughput on the order of 1 TB/hr on a multi-core Intel CPU, without the need for the GPU. The talk will also touch on other, related aspects of CPU engineering of a high-performance visual processing pipeline.
Speaker: Victor Jakubiuk, OnSpectra
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