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Video: NEC Accelerates Stanford DAWN Project with Vector Computing

Today NEC Corporation announced that it is supporting the Stanford DAWN project, an initiative to simplify the building of AI-powered applications, by providing a cluster of new “SX-Aurora TSUBASA” vector computers for research in the area of Artificial Intelligence. “We are very pleased to support the Stanford DAWN project. We hope to help demonstrate the value of vector computing to the advancement of AI domains,” said Yuichi Nakamura, Vice President, NEC Central Research Laboratories.

NEC Accelerates HPC with Vector Computing at ISC 2018

In this video from ISC 2018, Oliver Tennert from NEC Deutschland GmbH introduces the company’s vector computing technologies for HPC and Machine Learning. “The NEC SX-Aurora TSUBASA is the newest in the line of NEC SX Vector Processors with the worlds highest memory bandwidth. The Processor that is implemented in a PCI-e form factor can be configured in many flexible configurations together with a standard x86 cluster.”

Vectors are Back: The new NEC SX-Aurora TSUBASA Supercomputer

Today NEC Corporation launched their new high-end HPC product line, the SX-Aurora TSUBASA. “Utilizing cutting-edge chip integration technology, the new product features a complete multi-core vector processor in the form of a card-type Vector Engine (VE), which is developed based on NEC’s high-density interface technology and efficient cooling technology.”

NEC Vector Computers Accelerate Machine Learning

Today NEC Corporation announced that it has developed new Aurora Vector Engine data processing technology that accelerates the execution of machine learning on vector computers by more than 50 times in comparison to Spark technologies. “This technology enables users to quickly benefit from the results of machine learning, including the optimized placement of web advertisements, recommendations, and document analysis,” said Yuichi Nakamura, General Manager, System Platform Research Laboratories, NEC Corporation. “Furthermore, low-cost analysis using a small number of servers enables a wide range of users to take advantage of large-scale data analysis that was formerly only available to large companies.”