Jul 22, 2017   |   Jessica Rosenthal

Intel Demonstrates Latest AI & Computer Vision Tech at CVPR

The 2017 Conference on Computer Vision and Pattern Recognition (CVPR 2017) will be taking place on July 21-26th in Honolulu, Hawaii. CVPR is known as the premier annual computer vision event consisting of poster sessions, co-located workshops, and tutorials. Intel will have strong presence at the event through its Intel® Nervana™ Platinum Sponsorship, accepted research papers, EXPO…

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Jul 20, 2017   |   Gary Brown

Introducing: Movidius™ Neural Compute Stick

I’m thrilled to announce the availability of the Movidius™ Neural Compute Stick, a new device for developing and deploying deep learning algorithms at the edge. We created the Neural Compute Stick (NCS) to make deep learning application development on specialized hardware even more widely available. The NCS is powered by the same low-power Movidius Vision Processing…

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Jul 13, 2017   |   Scott Leishman

Introducing the aeon dataloader and other enhancements in Nervana Cloud 1.5.0

Nervana Cloud 1.5.0 contains enormous under-the-hood changes and improvements.  We’ve revamped and updated a lot of the core underlying code, separated the various application components into their own microservices, re-written our job launcher, added support for a new container orchestration service, squashed more than 75 bugs, and greatly expanded our testing coverage. The biggest changes…

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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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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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Jun 15, 2017   |   Naveen Rao

Comparing dense compute platforms for AI

In the world of artificial intelligence, there has been a lot of talk about performance and capabilities of hardware platforms.  It is true that today’s computing power is what allowed the AI revolution to (re)happen and this is a combination of 1) increased data set sizes, and 2) high-density compute.  In this blog, I’d like…

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May 17, 2017   |   Yinyin Liu

Partnership on AI

At Nervana and now at Intel, our data scientists work directly with domain experts to solve real-world problems using AI across a broad set of industries including agriculture, healthcare, automotive, energy, and finance. We spend our time building connections and applying deep learning to address each use case, and we are finding that the problem…

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#Company #News

Jan 06, 2017   |   Yinyin Liu

Building Skip-Thought Vectors for Document Understanding

The idea of converting natural language processing (NLP) into a problem of vector space mathematics using deep learning models has been around since 2013. A word vector, from word2vec [1], uses a string of numbers to represent a word’s meaning as it relates to other words, or its context, through training. From a word vector,…

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#Case Studies

Dec 29, 2016   |   Jennifer Myers

neon v1.8.0 released!

Highlights from this release include:  * Skip Thought Vectors example * Dilated convolution support * Nesterov Accelerated Gradient option to SGD optimizer * MultiMetric class to allow wrapping Metric classes * Support for serializing and deserializing encoder-decoder models * Allow specifying the number of time steps to evaluate during beam search * A new community-contributed Docker image…

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#Release Notes