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GTC On-Demand

Presentation
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Abstract:
We'll describe joint NVIDIA-Amazon work to build AI assistive tools for the caretakers of the Amazon Biospheres. We train deep autoencoders on time-series sensor streams for monitoring anomalies in the micro-climate. Our talk will cover how we deploy convolutional architectures for tracking plant stress levels using time-lapse vision models. We'll outline how we try to use best practices for edge-to-cloud AI and how we built a workflow to train models on EC2.P3 instances (NVIDIA Tesla V100 GPUs on AWS SageMaker). We'll also discuss how we optimize models for inference using TensorRT and subsequently deploy those models on NVIDIA Jetson TX2s for the biosphere.
We'll describe joint NVIDIA-Amazon work to build AI assistive tools for the caretakers of the Amazon Biospheres. We train deep autoencoders on time-series sensor streams for monitoring anomalies in the micro-climate. Our talk will cover how we deploy convolutional architectures for tracking plant stress levels using time-lapse vision models. We'll outline how we try to use best practices for edge-to-cloud AI and how we built a workflow to train models on EC2.P3 instances (NVIDIA Tesla V100 GPUs on AWS SageMaker). We'll also discuss how we optimize models for inference using TensorRT and subsequently deploy those models on NVIDIA Jetson TX2s for the biosphere.  Back
 
Topics:
AI and DL Research, Intelligent Machines and IoT, AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9627
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Abstract:

Survey of successful deep learning (DL) applications within several domains featuring continuous streaming data [ time-series ]. Overview of what network architectures have yielded results and why these networks work. Network architectures reviewed included: RNNs (dynamic models and prediction), CNNs (for frequency transformed time series data, i.e., spectrograms), Autoencoders (anomaly detection and unsupervised data-structure visualization), and deep MLPs (sliding window event detection and classification). Example case studies: Industrial { Industrial Robotics, Automotive Telematics, Prognostics/Zero-Down-Time }, IoT { Event & Anomaly Detection, Information Leakage Attacks/Defenses }, Financial { Limit Books, Mortgage Risk Markets}.

Survey of successful deep learning (DL) applications within several domains featuring continuous streaming data [ time-series ]. Overview of what network architectures have yielded results and why these networks work. Network architectures reviewed included: RNNs (dynamic models and prediction), CNNs (for frequency transformed time series data, i.e., spectrograms), Autoencoders (anomaly detection and unsupervised data-structure visualization), and deep MLPs (sliding window event detection and classification). Example case studies: Industrial { Industrial Robotics, Automotive Telematics, Prognostics/Zero-Down-Time }, IoT { Event & Anomaly Detection, Information Leakage Attacks/Defenses }, Financial { Limit Books, Mortgage Risk Markets}.

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Topics:
Intelligent Machines and IoT, Deep Learning and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7378
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Abstract:

Learn how to compile and run an optimized version of the MXNet deep learning framework for various embedded (IoT) devices, as well as see the wide range of exciting applications that running deep-network inference in near-realtime on "edge" devices opens up. Specifically, we'll be showing performance numbers for a variety of deep learning models based in MXNet running on Raspberry Pis as well as TK1 processors, demonstrating the massive efficiency gains on embedded devices MXNet yields over comparable frameworks. We'll then demo the power of real-time image processing via deep learning models with an example application walkthrough. Finally, we'll demonstrate how to use AWS IoT services to massively augment the flexibility and reliability of the models running in our example application.

Learn how to compile and run an optimized version of the MXNet deep learning framework for various embedded (IoT) devices, as well as see the wide range of exciting applications that running deep-network inference in near-realtime on "edge" devices opens up. Specifically, we'll be showing performance numbers for a variety of deep learning models based in MXNet running on Raspberry Pis as well as TK1 processors, demonstrating the massive efficiency gains on embedded devices MXNet yields over comparable frameworks. We'll then demo the power of real-time image processing via deep learning models with an example application walkthrough. Finally, we'll demonstrate how to use AWS IoT services to massively augment the flexibility and reliability of the models running in our example application.

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Topics:
Intelligent Machines and IoT, Deep Learning and AI, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7571
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