Intel Stretches Deep Learning on Scalable System Framework

May 10, 2016 Rob Farber The strong interest in deep learning neural networks lies in the ability of neural networks to solve complex pattern recognition tasks – sometimes better than humans. Once trained, these machine learning solutions can run very quickly – even in real-time – and very efficiently on low-power mobile devices and in the datacenter. However training a machine learning algorithm to accurately solve complex problems requires large amounts of data that greatly increases the computational workload. Scalable distributed parallel computing using a high-performance communications fabric is an essential part of what makes the training of deep learning on large complex datasets tractable in both the data center and within the cloud. Very simply, the single node TF/s parallelism delivered by Intel Xeon processor and Intel Xeon Phi…


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