Jun 14, 2018   |   Mahmoud Abuzaina, Mohammad Ashraf Bhuiyan, Wei Wang

Using Intel® Xeon® for Multi-node Scaling of TensorFlow* with Horovod*

TensorFlow* is one of the leading deep learning and machine learning frameworks today. Earlier in 2017, Intel worked with Google* to incorporate optimizations for Intel® Xeon® processor-based platforms using Intel® Math Kernel Library (Intel® MKL) [1].  Optimizations such as these with multiple popular frameworks have led to orders of magnitude improvement in performance. Intel has…

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#Technical Blog

Jan 24, 2018   |   Peng Zhang, Wei Wang, Baojun Liu, Jayaram Bobba

neon™ 2.6.0: Inference Optimizations for Single Shot MultiBox Detector on Intel® Xeon® Processor Architectures

We are excited to release the neon™ 2.6.0 framework, which features improvements for CPU inference path on a VGG-16 based Single Shot multibox Detector (SSD) neural network. These updates, along with the training optimizations released in neon 2.5.0, show that neon is gaining significant boosts in both training and inference performance.  (Granular configuration details, as well…

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#Release Notes

Nov 14, 2017   |   Wei Wang, Peng Zhang, Jayaram Bobba

neon v2.3.0: Significant Performance Boost for Deep Speech 2 and VGG models

We are excited to announce the release of neon™ 2.3.0.  It ships with significant performance improvements for Deep Speech 2 (DS2) and VGG models running on Intel® architecture (IA). For the DS2 model, our tests show up to 6.8X improvement1,4 with the  (Intel® MKL) backend over the NumPy CPU backend with neon™ 2.3.0, and more…

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#Release Notes

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