Join Lanner at NVIDIA’s GTC for a transformative global event that brings together brilliant, creative minds looking to ignite ideas, build new skills, and forge new connections to take on our biggest challenges. It all comes together online April 12 - 16, kicking off with NVIDIA CEO and Founder Jensen Huang’s keynote.
At GTC, Lanner will discuss how AI can be structured in a networked approach where AI workloads can be distributed within the edge networks. We will start from the NVIDIA AI-accelerated customer premise equipment over the aggregated network edge, to the hyper-converged platform deployed at the centralized datacenter.
Don’t miss out on this amazing event. Registration is free and gives you access to all the live sessions, interactive panels, demos, research posters, and more.
Lanner’s Keynotes at GTC21
Edge AI Inference and NGC-Ready Server: A Hardware Perspective [SS33111]
The accelerating deployment of powerful AI solutions in competitive markets has evolved hardware requirements down to the very edge of our network due to eruption in AI-based products and services. For edge AI workloads, efficient and high-throughput inference depends on a well-curated compute platform. Advanced AI applications now face fundamental deep learning inference challenges in latency, reliability, multi-precision artificial neural networks support and solution delivery. NGC software runs on a wide variety of edge-to-cloud GPU servers, and Lanner’s edge AI appliance, LEC-2290E, optimized for NVIDIA® T4 have passed an extensive suite of tests that validate its ability to deliver high-volume, low-latency inference using NVIDIA GPU and NGC software components such as TensorRT, TensorRT Inference Server, DeepStream, CUDA toolkit, and various NGC-supported deep learning frameworks.
AI Product and Market Development Manager, Lanner Electronics
SVP Engineering, Zippin
Building Efficient and Intelligent Networks Using Network Edge AI Platform [SS33127]
Edge computing requires multitasking workloads at the edge compute site in order to reduce communication latency, power, and real estate. As some of the workloads at the customer premises internet of things devices can leverage GPU functions for video processing, further analytics requires an open and scalable network platform for accelerated AI workloads at the service provider edge and even further analysis at a centralized data center platform. In this session, Lanner will partner with Tensor Network to discuss how NVIDIA AI can be structured in a networked approach where AI workloads can be distributed within the edge networks. We will start from the NVIDIA AI-accelerated customer premises equipment over the aggregated network edge and to the hyper-converged platform deployed at the centralized data center.
CTO of Telecom Applications BU, Lanner Electronics
CTO, Tensor Network
AI-Powered Hyper-Converged MEC Server Enables Intelligent Transportation Services [SS33040]
All innovative fleet services generate massive volumes of data, and that's driven fleet management companies' data centers to be more agile by integrating compute, storage, and networking into one hyper-converged infrastructure, which simplifies and consolidates all the virtualization components through software. With its software-defined nature, the hyper-converged infrastructure leverages existing hardware storage while adopting a virtual controller to manage the physical devices. Lanner came up with the hyper-converged MEC server that seamlessly integrates high performance computing, massive storage, and networking functions into one single appliance. Powered by NVIDIA T4 Tensor Core GPU, the MEC server consolidates the taxi management tasks, such as emergency call services, video surveillance systems, and location-based services. Highest Storage Density for FX-3420 can record all driving and service data for customer analysis and demand forecasting.
Director of Product Planning and Strategy , Lanner Electronics
Intelligent Edge Computing Box PC w/ Support for Intel® Core™ i7-8700T/i7-8700
|CPU||Support Intel® Core™ i7-8700T/i7-8700 Core i (FCLGA1152), Codenamed Coffee Lake S|