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In-Memory Neural Net Chip Cuts Data Movement
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…
NIST Photonics Chip Breaks New Ground and Models Neural Net
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…
Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019
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…
AI Self-Training Goes Forward at Google DeepMind
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…
AI Speeds Astrophysics Image Analysis by 10,000x
September 3, 2017
Since their earliest days, humans have gazed with wonder upon the firmaments and sought to understand the secrets of the heavenly canopy. In the late 20th centu Read more…
Here’s What a Neural Net Looks Like On the Inside
February 15, 2017
Ever wonder what the inside of a machine learning model looks like? Today Graphcore released fascinating images that show how the computational graph concept ma Read more…
AI’s Forward March: Machine Teaches Itself to Play Chess in 72 Hours
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…
IARPA Seeks Partners in Brain-Inspired AI Initiative
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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Powering Up Automotive Simulation: Why Migrating to the Cloud is a Game Changer
The increasing complexity of electric vehicles result in large and complex computational models for simulations that demand enormous compute resources. On-premises high-performance computing (HPC) clusters and computer-aided engineering (CAE) tools are commonly used but some limitations occur when the models are too big or when multiple iterations need to be done in a very short term, leading to a lack of available compute resources. In this hybrid approach, cloud computing offers a flexible and cost-effective alternative, allowing engineers to utilize the latest hardware and software on-demand. Ansys Gateway powered by AWS, a cloud-based simulation software platform, drives efficiencies in automotive engineering simulations. Complete Ansys simulation and CAE/CAD developments can be managed in the cloud with access to AWS’s latest hardware instances, providing significant runtime acceleration.
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Five Recommendations to Optimize Data Pipelines
When building AI systems at scale, managing the flow of data can make or break a business. The various stages of the AI data pipeline pose unique challenges that can disrupt or misdirect the flow of data, ultimately impacting the effectiveness of AI storage and systems.
With so many applications and diverse requirements for data types, management systems, workloads, and compliance regulations, these challenges are only amplified. Without a clear, continuous flow of data throughout the AI data lifecycle, AI models can perform poorly or even dangerously.
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