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GPU Acceleration for 5G Signal Processing and Machine Learning
September 15, 2021 @ 8:00 am - 12:00 pm
Welcome to the [SPS Seasonal School](https://signalprocessingsociety.org/blog/2021-ieee-sps-seasonal-school-signal-processing-and-communication-systems-5g) on Signal Processing and Communication Systems for 5G. The jointly, internationally, organized by IEEE Vizag Bay Section [Communications Society & SPS](https://r10.ieee.org/vizagbay/) Joint Chapter, Vizag, India, IEEE Long Island Section ([SPS](http://www.ieee.li/sp)) Chapter, New York, USA, and IEEE [Finland SP/CAS](https://site.ieee.org/finland/) Chapter, Finland presents the following technical lecture:
Abstract:
As the rollout of 5G progresses and research for 6G begins, the key themes of softwarization, virtualization, open systems and artificial intelligence form foundational principles for communication systems of the future. The application of AI/MLto wireless communications an extremely active research area with many 10’s to 100’s of papers published weekly reporting new results on the application of AI/ML to the physical layer (L1), MAC layer (L2) and at the network optimization level.
To realize the Industry’s vision of an AI/ML powered wireless future, a full stack solution supporting a software defined radio (SDR) approach for the vRAN, together with optimized silicon for AI, coupled with application development frameworks for AI/ML development is essential. NVIDIA GPU technology and associated CUDA programming model, together with arch suite of AI/ML SDKs (Software Development Kits) provides these capabilities.
In this talk we present The Aerial software-defined GPU-based cloud native 5G NR RAN platform. Aerial implements not only 5G NR the baseband signal processing, but using GPU virtualization supports additional concurrently operating workloads, such as AI/ML inference, training and data analytics on this one hyper-converged system. We provide an overview of the L1 signal processing pipeline and describe efficient mechanisms for data movement between the GPU and NIC-based fronthaul interface using a GPU-enabled Data Plane Development Kit (DPDK). A brief survey of some of the promising deep learning approaches for L1 and L2enhancements is presented.
Speaker(s): Dr. Chris Dick ,
Virtual: https://events.vtools.ieee.org/m/269884
