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Volta Tensor Core GPU Achieves New AI Performance Milestones

Artificial intelligence powered by deep learning now solves challenges once thought impossible, such as computers understanding and conversing in natural speech and autonomous driving. Inspired by the effectiveness of deep learning to solve a great many challenges, the exponentially growing complexity of algorithms has resulted in a voracious appetite for faster computing. NVIDIA designed the Volta Tensor Core architecture to meet these needs.

NVIDIA and many other companies and researchers have been developing both computing hardware and software platforms to address this need. For instance, Google created their TPU (tensor processing unit) accelerators which have generated good performance on the limited number of neural networks that can run on TPUs.

In this blog, we share some of our recent advancements which deliver dramatic performance gains on GPUs to the AI community. We have achieved record-setting ResNet-50 performance for a single chip and single server with these improvements. Recently, fast.ai also announced their record-setting performance on a single cloud instance.

Our results demonstrate that:

  • A single V100 Tensor Core GPU achieves 1,075 images/second when training ResNet-50, a 4x performance increase compared to the previous generation Pascal GPU.
  • A single DGX-1 server powered by eight Tensor Core V100s achieves 7,850 images/second, almost 2x the 4,200 images/second from a year ago on the same system.
  • A single AWS P3 cloud instance powered by eight Tensor Core V100s can train ResNet-50 in less than three hours, 3x faster than a TPU instance.

 

Volta Tensor Core GPU ResNet-50 record
Figure 1. Volta Tensor Core GPU Achieves Speed Records In ResNet-50 (AWS P3.16xlarge instance consists of 8x Tesla V100 GPUs).

Massive parallel processing performance on a diversity of algorithms makes NVIDIA GPUs naturally great for deep learning. We didn’t stop there. Tapping our years of experience and close collaboration with AI researchers all over the world, we created a new architecture optimized for the many models of deep learning – the NVIDIA Tensor Core GPU.

Combined with high-speed NVLink interconnect plus deep optimizations within all current frameworks, we achieve state-of-the-art performance. NVIDIA CUDA GPU programmability ensures performance for the large diversity of modern networks, as well as provides a platform to bring up emerging frameworks and tomorrow’s deep network inventions  …..
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Machine Learning’s ‘Amazing’ Ability to Predict Chaos

In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.
Gif illustration for "Machine Learning’s ‘Amazing’ Ability to Predict Chaos"

Researchers have used machine learning to predict the chaotic evolution of a model flame front.

Half a century ago, the pioneers of chaos theory discovered that the “butterfly effect” makes long-term prediction impossible. Even the smallest perturbation to a complex system (like the weather, the economy or just about anything else) can touch off a concatenation of events that leads to a dramatically divergent future. Unable to pin down the state of these systems precisely enough to predict how they’ll play out, we live under a veil of uncertainty.

In a series of results reported in the journals Physical Review Letters and Chaos, scientists have used machine learning — the same computational technique behind recent successes in artificial intelligence — to predict the future evolution of chaotic systems out to stunningly distant horizons. The approach is being lauded by outside experts as groundbreaking and likely to find wide application.

“I find it really amazing how far into the future they predict” a system’s chaotic evolution, said Herbert Jaeger, a professor of computational science at Jacobs University in Bremen, Germany  …. <more>

Graphic illustration depicting the charts of training computers to predict chaos.

Full pages:

https://www.wired.com/story/machine-learnings-amazing-ability-to-predict-chaos/

https://www.quantamagazine.org/machine-learnings-amazing-ability-to-predict-chaos-20180418/

 

 

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Microsoft researchers build a bot that draws what you tell it to

If you’re handed a note that asks you to draw a picture of a bird with a yellow body, black wings and a short beak, chances are you’ll start with a rough outline of a bird, then glance back at the note, see the yellow part and reach for a yellow pen to fill in the body, read the note again and reach for a black pen to draw the wings and, after a final check, shorten the beak and define it with a reflective glint. Then, for good measure, you might sketch a tree branch where the bird rests.

Now, there’s a bot that can do that, too.

The new artificial intelligence technology under development in Microsoft’s research labs is programmed to pay close attention to individual words when generating images from caption-like text descriptions. This deliberate focus produced a nearly three-fold boost in image quality compared to the previous state-of-the-art technique for text-to-image generation, according to results on an industry standard test reported in a research paper posted on arXiv.org.

The technology, which the researchers simply call the drawing bot, can generate images of everything from ordinary pastoral scenes, such as grazing livestock, to the absurd, such as a floating double-decker bus. Each image contains details that are absent from the text descriptions, indicating that this artificial intelligence contains an artificial imagination.

Continue reading: https://blogs.microsoft.com/ai/drawing-ai/

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A Novel Algorithm Enables Statistical Analysis of Time Series Data

MIT scientists have developed a novel approach to analyzing time series data sets using a new algorithm, termed state-space multitaper time-frequency analysis (SS-MT). SS-MT gives a structure to dissect time arrangement information progressively, empowering analysts to work in a more educated manner with extensive arrangements of information that are nonstationary, i.e. at the point when their qualities develop after some time.

A Novel Algorithm Enables Statistical Analysis of Time Series Data

Using a novel analytical method they have developed, MIT researchers analyzed raw brain activity data (B). The spectrogram shows decreased noise and increased frequency resolution, or contrast (E and F) compared to standard spectral analysis methods (C and D). Image courtesy of Seong-Eun Kim et al.

It is important to measure time while every task such as tracking brain activity in the operating room, seismic vibrations during an earthquake, or biodiversity in a single ecosystem over a million years. Measuring the recurrence of an event over some stretch of time is a major information investigation errand that yields basic knowledge in numerous logical fields.

This newly developed approach enables analysts to measure the moving properties of information as well as make formal factual correlations between discretionary sections of the information.

Emery Brown, the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience said, “The algorithm functions similarly to the way a GPS calculates your route when driving. If you stray away from your predicted route, the GPS triggers the recalculation to incorporate the new information.”

“This allows you to use what you have already computed to get a more accurate estimate of what you’re about to calculate in the next time period. Current approaches to analyses of long, nonstationary time series ignore what you have already calculated in the previous interval leading to an enormous information loss.”  …… 

Full post: https://www.techexplorist.com/novel-algorithm-enables-statistical-analysis-time-series-data/

Abstract: http://www.pnas.org/content/early/2017/12/15/1702877115