ON-DEMAND
Webinar

GenAI for the Edge: Harness the Power of LLMs on Edge Devices

Virtual
Booth
August 1, 2024
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Date & Time
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August 1, 2024
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Location
Virtual

The latest Large Language Models (LLMs) offer staggering multi-modal capabilities. And while they are typically much too massive to run locally on small devices, new functionality in Edge Impulse provides a novel solution to utilize the power of an LLM on an edge device.

In this webinar, join Edge Impulse’s CTO and co-founder, Jan Jongboom, to explore innovations in leveraging LLMs for ultra-compact edge AI models. Discover how to harness the powerful capabilities of LLMs to automatically analyze and label visual data and how to apply the LLM’s specific understanding of what's in frame without needing someone to manually input any labels. Efficiently create deployable object detection models that are 2,000,000x smaller, and can run seamlessly on any edge device.

Register for the webinar to:

  • Learn about the latest developments in GenAI applications for the edge 
  • Learn how to distill knowledge from an LLM to significantly smaller models that operate locally
  • Discover advanced visual models like NVIDIA TAO, along with optimization techniques
  • Learn about using GenAI to create synthetic data (visual, sound effects, and keywords) to supplement your datasets using NVIDIA Omniverse and ElevenLabs
Our Presence:
Speaking Sessions:

Jan Jongboom

Jan Jongboom is an embedded engineer and machine learning advocate, always looking for ways to gather more intelligence from the real world. He has shipped devices, worked on the latest network tech, simulated microcontrollers and there's a monument in San Francisco with his name on it. Currently he serves as the cofounder and CTO of Edge Impulse, the leading development platform for embedded machine learning with 140,000+ projects.

Join us on 25 April at 12:20-12:35 at the Embedded Park and Speakers Center for a presentation from Jim Bruges, Edge Impulse solutions engineer.

Jim Bruges is a seasoned Solutions Engineer at Edge Impulse, a company leading the charge in Edge AI innovation. Working with customers across industries including healthcare, industrial IOT, wearables and more, Jim has seen how Edge AI can be brought into today's products to provide valuable insights and meaning to the data these devices produce. By applying his previous experience in a wide range of engineering disciplines, including 4 years at Dyson, he advises clients about AI at the Edge and is an expert in Edge use-cases.

How to Train an Object Detection Model for Visual Inspection with Synthetic Data

Jenny Plunkett is a software engineer, technical speaker and content creator, working as a Senior Developer Relations Engineer at Edge Impulse. Since graduating from The University of Texas she has been working in the IoT space, from customer engineering and developer support for Arm Mbed to consulting engineering for Pelion IoT. Jenny is co-author of the O’Reilly book "AI at the Edge: Solving Real World Problems with Embedded Machine Learning".

GTC 2024: Jetson and Robotics Developer Day

Jenny Plunkett is a software engineer, technical speaker and content creator, working as a Senior Developer Relations Engineer at Edge Impulse. Since graduating from The University of Texas she has been working in the IoT space, from customer engineering and developer support for Arm Mbed to consulting engineering for Pelion IoT. Jenny is co-author of the O’Reilly book "AI at the Edge: Solving Real World Problems with Embedded Machine Learning".

Swift Path to Edge AI: Revolutionizing Development Through Synthetic Data

Jenny Plunkett is a software engineer, technical speaker and content creator, working as a Senior Developer Relations Engineer at Edge Impulse. Since graduating from The University of Texas she has been working in the IoT space, from customer engineering and developer support for Arm Mbed to consulting engineering for Pelion IoT. Jenny is co-author of the O’Reilly book "AI at the Edge: Solving Real World Problems with Embedded Machine Learning".

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