As Large Language Models (LLMs) and other generative AI systems continue to push the boundaries of what’s possible with AI, we’re witnessing a paradigm shift in how these powerful tools can be deployed on edge devices. Edge Impulse is at the forefront of GenAI for the edge revolution, leveraging LLMs to create ultra-compact and powerful edge AI models.
Here's a roundup of articles on how Edge Impulse is employing GenAI for the edge:
Introducing Synthetic Data Generation in Edge Impulse
Data generation
We are proud to announce that synthetic data generation is now available inside the Edge Impulse platform, enabling a new and efficient way to work with LLM-generated images, audio, and voice to enhance your edge AI models.
Bringing Large Language Models to the Edge with GPT-4o and NVIDIA TAO
Data collection, data labeling, data processing
Here's how to use an LLM's understanding to train a much smaller model that can run rapidly and accurately directly on an MCU.
Easy Data Cleanup with Generative AI
Data cleansing
Using Edge Impulse's newest transformation block, validate your data in minutes and create high quality datasets through the power of multi-modal LLMs.
Using LLMs to Analyze and Label Satellite Imagery in Edge Impulse
Data labeling
With Edge Impulse's newest feature, visual data from a satellite can be labeled via GPT-4o both for cloud type and for coverage, generating two useful edge models from the same dataset very quickly.
That Sounds Great: Create Ultra-Realistic Audio Datasets with ElevenLabs.io
Data generation
The new ElevenLabs.io integration enables you to expand your Edge Impulse datasets with sounds that are difficult or expensive to record naturally, using state-of-the-art generative techniques.
Improving Camera Traps to Identify Unknown Species with GPT-4o
Data generation, data labeling
When deploying AI models for wildlife monitoring to remote cameras in the field, a common challenge is dealing with unexpected animal species that were not accounted for during training. Here's an approach to improve that.
Using OpenAI Whisper to Train a Tiny Keyword Spotting Model— in Any Language
Data generation
Need to spot keywords in a language you don't speak? Here's how to easily use Edge Impulse to get all the voice samples you need, train the model, and deploy it to extremely tiny MCUs to run on-device.
Automate Your Edge AI Audio Dataset Labeling with Edge Impulse and Hugging Face
Automatic data labeling (audio)
Building an Edge AI audio classification model involves collecting and labeling a lot of real-world data—an often tedious and time-consuming task. Edge Impulse enables developers a way to skip those tedious hours, and do it automatically with the Edge Impulse transformation block.
More ways Edge Impulse is using synthetic data, with NVIDIA Omniverse:
New Integrations: Edge Impulse Optimizes NVIDIA AI for the Edge
Unlock previously inaccessible NVIDIA AI capabilities for any edge device with NVIDIA TAO and Omniverse, alongside our launch of native support for NVIDIA Jetson Orin hardware.
Icicle Detection with NVIDIA Omniverse — Cracking a Cold Case with Synthetic Data
While they might seem picturesque, hanging icicles can pose significant dangers and practical challenges for property owners. One engineer used Edge Impulse and synthetic data from NVIDIA Omniverse to build a warning system.
No Scalpel Left Behind: Tracking Surgical Tools with AI and synthetic Data from NVIDIA Omniverse
Object-detection models that can recognize shiny tools under spotlights require a very large variety of image data. Enter NVIDIA Omniverse Replicator.
Object Detection: Fake It to Make It
Create synthetic data to rapidly build object detection datasets with NVIDIA Omniverse’s Replicator API and Edge Impulse.
Pushing the boundaries of edge AI has never been easier; the breakthroughs in ultra-compact LLMs on edge-optimized generative models are just the beginning. By bringing the power of GenAI to edge devices, users are not just improving performance — they are reimagining the very fabric of embedded intelligence.