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Baidu Research Announces DeepBench Benchmark for Deep Learning

“Deep learning developers and researchers want to train neural networks as fast as possible. Right now we are limited by computing performance,” said Dr. Diamos. “The first step in improving performance is to measure it, so we created DeepBench and are opening it up to the deep learning community. We believe that tracking performance on different hardware platforms will help processor designers better optimize their hardware for deep learning applications.”

Call for Proposals: Fortissimo Project

The European Fortissimo Project has issued its Second Call for Proposals. Fortissimo is a collaborative project that enables European SMEs to be more competitive globally through the use of simulation services running on High Performance Computing Cloud infrastructure.

ArrayFire v3.4 Parallel Computing Library Speeds Machine Learning

Today ArrayFire released the latest version of their ArrayFire open source library of parallel computing functions supporting CUDA, OpenCL, and CPU devices. ArrayFire v3.4 improves features and performance for applications in machine learning, computer vision, signal processing, statistics, finance, and more.

Video: The Deep Learning AI Revolution

In this video from GTC 2016 in Taiwan, Nvidia CEO Jen-Hsun Huang unveils technology that will accelerate the deep learning revolution that is sweeping across industries. “AI computing will let us create machines that can learn and behave as humans do. It’s the reason why we believe this is the beginning of the age of AI.”

Video: CAT Supercomputer to Track Dark Pools on Wall Street

In this video, Better Markets CEO Dennis Kelleher discusses the progress of the Consolidated Audit Trail (CAT), a proposed SEC supercomputer that will be used to track orders and peer into dark pools. While this sounds like a good idea, Kelleher describes the conflicts of interest inherent in the proposal process the SEC is using for CAT. Kelleher is the CEO of Better Markets, a non-profit, non-partisan, and independent organization founded in the wake of the 2008 financial crisis to promote the public interest in the financial markets.

Video: HPC Disruptive Technologies Panel

In this video from the 2016 HPC User Forum in Austin, a select panel of HPC vendors describe their disruptive technologies for high performance computing. Vendors include: Altair, SUSE, ARM, AMD, Ryft, Red Hat, Cray, and Hewlett Packard Enterprise. “A disruptive innovation is an innovation that creates a new market and value network and eventually disrupts an existing market and value network, displacing established market leading firms, products and alliances.”

MathWorks Release 2016b Makes it Easier to Work with Big Data

“Companies are awash in data, but struggle to take advantage of it to build better predictive models and gain deeper insights,” says David Rich, MATLAB marketing director, MathWorks. “With R2016b, we’ve lowered the bar to allow domain experts to work with more data, more easily. This leads to improved system design, performance, and reliability.”

Nvidia Expands Deep Learning Institute

Over at the Nvidia Blog, Jamie Beckett writes that the company’s is expanding its Deep Learning Institute with Microsoft and Coursera. The institute provides training to help people apply deep learning to solve challenging problems.

Nvidia Unveils World’s First GPU Design for Inferencing

Nvidia’s GPU platforms have been widely used on the training side of the Deep Learning equation for some time now. Today the company announced a new Pascal-based GPU tailor-made for the inferencing side of Deep Learning workloads. “With the Tesla P100 and now Tesla P4 and P40, NVIDIA offers the only end-to-end deep learning platform for the data center, unlocking the enormous power of AI for a broad range of industries,” said Ian Buck, general manager of accelerated computing at NVIDIA.”

Examples of Deep Learning Industrialization

Humans are very good at visual pattern recognition especially when it comes to facial features and graphic symbols and identifying a specific person or associating a specific symbol with an associated meaning. It is in these kinds of scenarios where deep learning systems excel. Clearly identifying each new person or symbol is more efficiently achieved by a training methodology than by needing to reprogram a conventional computer or explicitly update database entries.