Industrial AI & Machine Lear1ing
 
NVIDIA?s $99 Jetson Nano Skimps on Size and Power, But Not Performance
BRANDON LEWIS, EDITOR-IN-CHIEF
The Jetson Nano is an 80 mm x 100 mm developer kit based on a Tegra SoC with a 128-core Maxwell GPU and quad-core Arm Cortex-A57 CPU. This gives the Nano a reported 472 GFLOPS of compute horsepower, which can be harnessed within configurable power modes of 5W or 10W.
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Medical sensor technology continues to advance at a rapid pace, allowing compact, cost-effective, and increasingly accurate physiological sensors to make their way into off-the-shelf wearable devices. One of the real drivers of this transformation is the availability of cutting-edge machine learning and AI algorithms that can extract and interpret meaningful information from vast troves of data.
 
 
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To build machine learning algorithms, which many predictive maintenance systems rely on, there must be enough data to create an accurate model. This data usually originates from sensors on machinery, but companies can run into issues when data collection is not an option, when using new sensors, or when data readings are incorrectly logged, and information is limited.
 
 
 
 
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Cognata, Ltd. is working with NVIDIA to deliver various scenario and traffic models for validation employing large-scale, hardware-in-the-loop simulation. The companies exhibited the integration at GTC this week in booth #149 at the San Jose McEnery Convention Center, March 18-21.
 
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Deep Learning consists of the sub-areas training and inference. In the training phase, a training model is developed, tested and refined to the desired accuracy using a comprehensive dataset of images and videos. While in the inference phase a rapid and reliable deployment of the training model in the target environment is the focus.
 
 
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TYAN made an appearance at GTC this week to display a variety of server platforms that support the NVIDIA T4, NVIDIA V100 Tensor Core, and NVIDIA Quadro RTX 6000 GPU accelerators for multiple compute-intensive workloads including deep learning training, inference, and photorealistic rendering.
 
 
 
 
 
 
Sponsored by: Texas Instruments
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Process automation is enabling a level of accuracy and productivity that goes beyond human ability. And one critical area in the factory where automation is making a big difference is quality assurance.
 
 
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The growth of IIoT means that more systems need to run enterprise applications alongside real-time processes. This puts mixed-criticality workloads on a single device, which can compromise the determinism of industrial workloads.
 
 
 
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