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GTC ON-DEMAND

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Abstract:
Radio frequency (RF) systems have become increasingly complex, and the number of connected devices is expected to increase. We'll discuss how deep learning within RF shows promise for dealing with a congested spectrum by enhancing reliability and simplifying the task of building effective wireless systems. Deep learning algorithms within RF technology show superior results, classifying signals well below the noise floor when compared to traditional signal processing methods. We'll describe how we've worked with partners to design a software-configurable wide-band RF transceiver system that can perform real-time DSP and deep learning with an NVIDIA GPU. We'll discuss RF system performance, RF training data collection, and software used to create applications. Additionally, we will present data demonstrating applications in deep learning enabled-RF technology.
Radio frequency (RF) systems have become increasingly complex, and the number of connected devices is expected to increase. We'll discuss how deep learning within RF shows promise for dealing with a congested spectrum by enhancing reliability and simplifying the task of building effective wireless systems. Deep learning algorithms within RF technology show superior results, classifying signals well below the noise floor when compared to traditional signal processing methods. We'll describe how we've worked with partners to design a software-configurable wide-band RF transceiver system that can perform real-time DSP and deep learning with an NVIDIA GPU. We'll discuss RF system performance, RF training data collection, and software used to create applications. Additionally, we will present data demonstrating applications in deep learning enabled-RF technology.  Back
 
Topics:
Accelerated Data Science, Telecommunications, AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9792
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Abstract:

Deep learning continues to show benefit in significant aspects of sensor systems including computer vision, speech recognition, and cybersecurity. In parallel, radio frequency (RF) systems have become increasingly complex and the number of connected devices will significantly increase as IoT and 5G systems become prevalent. Deep learning within RF systems is a new field of research that shows promise for dealing with a congested spectrum, brining reliability enhancements, and simplifying the ability to build effective signal processing systems. The utilization of deep learning algorithms within RF technology has shown superior results and the ability to classify signals well below the noise floor when compared to traditional signal processing methods. Working with strategic partners, we have designed a software configurable wide-band RF transceiver system capable of performing real-time signal processing and deep learning with an NVIDIA Jetson TX2. We discuss RF specific system performance, collection of RF training data, and the software used by the community to create custom applications. Additionally, we will present data demonstrating applications in the field of deep learning enabled RF systems.

Deep learning continues to show benefit in significant aspects of sensor systems including computer vision, speech recognition, and cybersecurity. In parallel, radio frequency (RF) systems have become increasingly complex and the number of connected devices will significantly increase as IoT and 5G systems become prevalent. Deep learning within RF systems is a new field of research that shows promise for dealing with a congested spectrum, brining reliability enhancements, and simplifying the ability to build effective signal processing systems. The utilization of deep learning algorithms within RF technology has shown superior results and the ability to classify signals well below the noise floor when compared to traditional signal processing methods. Working with strategic partners, we have designed a software configurable wide-band RF transceiver system capable of performing real-time signal processing and deep learning with an NVIDIA Jetson TX2. We discuss RF specific system performance, collection of RF training data, and the software used by the community to create custom applications. Additionally, we will present data demonstrating applications in the field of deep learning enabled RF systems.

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Topics:
Autonomous Machines and IoT, Deep Learning and AI
Type:
Talk
Event:
GTC Washington D.C.
Year:
2018
Session ID:
DC8103
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Abstract:
Artificial intelligence has made great strides in many technology sectors, however, it has yet to impact the design and applications of radio frequency (RF) and wireless systems. This is primarily due to the industry''s preference towards field programmable gate array (FPGA) systems. Conversely, the deep learning revolution has been fueled by GPUs and the ease with which they may be programmed for highly parallel computations. The next generation RF and wireless technology will require wide-band systems capable of real-time machine learning with GPUs. Working with strategic partners, we''ve designed a software configurable wide-band RF transceiver system capable of performing real-time signal processing and machine learning with a Jetson TX2. We discuss system performance, collection of RF training data, and the software used by the community to create custom applications. Additionally, we''ll present data demonstrating applications in the field of RF machine learning and deep learning.
Artificial intelligence has made great strides in many technology sectors, however, it has yet to impact the design and applications of radio frequency (RF) and wireless systems. This is primarily due to the industry''s preference towards field programmable gate array (FPGA) systems. Conversely, the deep learning revolution has been fueled by GPUs and the ease with which they may be programmed for highly parallel computations. The next generation RF and wireless technology will require wide-band systems capable of real-time machine learning with GPUs. Working with strategic partners, we''ve designed a software configurable wide-band RF transceiver system capable of performing real-time signal processing and machine learning with a Jetson TX2. We discuss system performance, collection of RF training data, and the software used by the community to create custom applications. Additionally, we''ll present data demonstrating applications in the field of RF machine learning and deep learning.  Back
 
Topics:
AI Application Deployment and Inference, NVIDIA Inception Program, Cyber Security, IoT, Robotics & Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8375
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