Blog

Nov 14, 2017   |   Wei Wang, Peng Zhang, Rong 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® Math Kernel Library (Intel® MKL) backend over the NumPy CPU backend with…

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#neon

BDW-SKX Normalized Throughput

Sep 18, 2017   |   Jayaram Bobba

neon v2.1.0: Leveraging Intel® Advanced Vector Extensions 512 (Intel® AVX-512)

We are excited to announce the availability of neon™ 2.1 framework. An optimized backend based on Intel® Math Kernel Library (Intel® MKL), is enabled by default on CPU platforms with this release. neon™ 2.1 also uses a newer version of the Intel ® MKL for Deep Neural Networks (Intel ® MKL-DNN), which features optimizations for…

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#neon #Release Notes

Jun 28, 2017   |   Jayaram Bobba

neon™ 2.0: Optimized for Intel® Architectures

neon™ is a deep learning framework created by Nervana Systems with industry leading performance on GPUs thanks to its custom assembly kernels and optimized algorithms. After Nervana joined Intel, we have been working together to bring superior performance to CPU platforms as well. Today, after the result of a great collaboration between the teams, we…

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#neon

Jun 22, 2017   |   Jason Knight

Intel® Nervana™ Graph Beta

We are building the Intel Nervana Graph project to be the LLVM for deep learning, and today we are excited to announce a beta release of our work we previously announced in a technical preview. We see the Intel Nervana Graph project as the beginning of an ecosystem of optimization passes, hardware backends and frontend…

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#Intel Nervana Graph #neon

Jun 19, 2017   |   Urs Köster

Training Generative Adversarial Networks in Flexpoint

Training Generative Adversarial Networks in Flexpoint With the recent flood of breakthrough products using deep learning for image classification, speech recognition and text understanding, it’s easy to think deep learning is just about supervised learning. But supervised learning requires labels, which most of the world’s data does not have. Instead, unsupervised learning, extracting insights from…

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Dec 08, 2016   |   Anthony Ndirango

End-to-end speech recognition with neon

By: Anthony Ndirango and Tyler Lee Speech is an intrinsically temporal signal. The information-bearing elements present in speech evolve over a multitude of timescales. The fine changes in air pressure at rates of hundreds to thousands of hertz convey information about the speakers, their location, and help us separate them from a noisy world. Slower changes in…

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Nov 22, 2016   |   Jennifer Myers

neon v1.7.0 released!

Highlights from this release include:  Update Data Loader to aeon for flexible, multi-threaded data loading and transformations. More information can be found in the docs, but in brief, aeon: provides an easy interface to adapt existing models to your own, custom, datasets supports images, video and audio and is easy to extend with your own providers for custom…

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#neon #Release Notes

Oct 12, 2016   |   Sathish Nagappan

Accelerating Neural Networks with Binary Arithmetic

At Nervana we are deeply interested in algorithmic and hardware improvements for speeding up neural networks. One particularly exciting area of research is in low precision arithmetic. In this blog post, we highlight one particular class of low precision networks named binarized neural networks (BNNs), the fundamental concepts underlying this class, and introduce a Neon…

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#neon

Sep 22, 2016   |   Jennifer Myers

neon v1.6.0 released!

Highlights from this release include:  Faster RCNN model Sequence to Sequence container and char_rae recurrent autoencoder model Reshape Layer that reshapes the input[#221] Pip requirements in requirements.txt updated to latest versions [#289] Remove deprecated data loaders and update docs Use NEON_DATA_CACHE_DIR envvar as archive dir to store DataLoader ingested data Eliminate type conversion for FP16…

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#neon #Release Notes

Jul 25, 2016   |   Scott Gray

Still not slowing down: Benchmarking optimized Winograd implementations

By: Scott Gray and Urs Köster This is part 3 of a series of posts on using the Winograd algorithm to make convolutional networks faster than ever before. In the second part we provided a  technical overview of how the algorithm works. Since the first set of Winograd kernels in neon, which we described in…

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#neon

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