October 8, 2018
A university-industry research team is reporting a performance advance for neural networks with the development of a chip with potential applications for image recognition in autonomous vehicles and robots. The chip design relies on in-memory processing and the replacement of standard transistors with capacitors used to store electrical charges. Read more…
August 7, 2018
Researchers at the National Institute of Standards and Technology (NIST) have made a silicon chip that distributes optical signals precisely across a miniature Read more…
May 24, 2018
At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…
October 19, 2017
DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…
September 23, 2015
The field of artificial intelligence has had a rocky history with numerous setbacks, but there have been high points too, like when IBM's Deep Blue beat reignin Read more…
January 22, 2015
US intelligence officials have set in motion a five-year project to spark progress in machine learning by reverse-engineering the algorithms of the human brain. Read more…
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A workload-driven system capable of running HPC/AI workloads is more important than ever. Organizations face many challenges when building a system capable of running HPC and AI workloads. There are also many complexities in system design and integration. Building a workload driven solution requires expertise and domain knowledge that organizational staff may not possess.
This paper describes how Quanta Cloud Technology (QCT), a long-time Intel® partner, developed the Taiwania 2 and Taiwania 3 supercomputers to meet the research needs of the Taiwan’s academic, industrial, and enterprise users. The Taiwan National Center for High-Performance Computing (NCHC) selected QCT for their expertise in building HPC/AI supercomputers and providing worldwide end-to-end support for solutions from system design, through integration, benchmarking and installation for end users and system integrators to ensure customer success.
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