Quantum computing pioneer D-Wave Systems has announced some details of a new 2000-qubit system it has developed. The processor that drives the system will contain twice the number of qubits that powers the current D-Wave 2X system, which was announced last year. The new hardware also includes additional control features that enables users to tune the quantum computations to arrive at a faster result and to explore the solution space more fully.
TrueNorth, IBM’s brain-like microprocessor, has been found to be exceptionally proficient at inference work for deep neural networks. In particular, the chip has demonstrated it’s especially good at image recognition, being able accurately classify such data much more efficiently, from an energy perspective, than traditional processor architectures, suggesting new applications in mobile computing, IoT, robotics, autonomous cars, and HPC.
BenevolentAI, a London-based artificial intelligence company specializing in health and bioscience applications, has acquired NVIDIA’s DGX-1, a deep learning system accelerated by eight Tesla P100 GPUs. The company plans to use the $129,000 machine to advance its work in drug discovery and related biomedical research.
Researchers at the University of Basel in Switzerland have used machine learning to predict the thermodynamic characteristics of 90 new mineral compounds with potential commercial use. The machine learning models were able to predict the chemical stability of all possible iterations of a particular type of class of crystals several orders of magnitude faster than if the researchers had relied on quantum mechanical calculations.
The Texas Advanced Computing Center in Austin has installed the world’s largest solar-powered HPC system. The 400 teraflop supercomputer, known as Hikari, is an HPE Apollo 800 cluster that uses technology supplied by Japanese green energy specialist NTT FACILITIES. In addition to taking advantage of Austin’s abundant sunshine, the power setup also employs high voltage direct power (HVDC) to further reduce energy consumption.
In these days of Apple Siri, Amazon Alexa, and Google Now, AI-based speech generation has become a commonplace technology in everyday life. But when machines talk to us, it’s still obvious that we are listening to a computer, and not a human voice. Thanks to new machine learning technology from Alphabet's DeepMind, that may soon change.
Last week the US DOE awarded $39.8 million worth of funding for more than a dozen application development projects aimed at exascale computing. A total of 15 proposals were funded, with an additional 7 receiving seed funding. The awards represent the first significant sum of federal money directed at developing exascale applications in the US.
The rumors about a setback in the development of Japan’s first exascale system have apparently been borne out. This week at the HPC User Forum in Austin, Texas, Dr. Yutaka Ishikawa, project lead for the Post-K supercomputer at RIKEN, admitted the effort will be delayed by one to two years.
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