Intel® Xeon Phi™ Delivers Competitive Performance For Deep Learning—And Getting Better Fast

Baidu’s recently announced deep learning benchmark, DeepBench, documents performance for the lowest-level compute and communication primitives for deep learning (DL) applications. The goal is to provide a standard benchmark to evaluate different hardware platforms using the vendor’s DL libraries. Intel continues to optimize its Intel® Xeon and Intel® Xeon Phi™ processors for DL via the Intel Math Kernel Library (Intel MKL). Intel MKL 2017 includes a collection of performance primitives for DL applications. The library supports the most commonly used primitives necessary to accelerate image recognition topologies and GEMM primitives necessary to accelerate various types of RNNs. The functionality includes convolution, inner product, pooling, normalization and activation primitives, with support for forward (inference) and backward (gradient propagation) operations. The MKL 2017 library is freely available with a community license, and…


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