Edge Impulse empowers ML teams to run AI at peak performance on any edge hardware, with unmatched ease and speed.
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ML-Powered Agricultural Robotics
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Case Study
Agriculture
AI-Managed Crops Irrigation
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Case Study
Agriculture
Smart Grid Monitoring
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Case Study
Energy Management
IoT Gets an ML Boost, From Edge to Cloud
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Case Study
Smart IoT
AI-Automated Hard-Hat Detection
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Case Study
Industrial
Building Safer Roads
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Case Study
Transportation
Cold Chain Monitoring
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Case Study
Supply Chain/Logistics
Lexmark
Adding AI to Optra Edge
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Case Study
Supply Chain/Logistics
Sony
Predictive Maintenance with Sony Spresense and CommonSense
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Case Study
Predictive Maintenance
Making Strides in Personalized Healthcare
In order to consistently detect unusual gait patterns, better diagnostic tools are needed. One engineer enlisted Edge Impulse and the Nordic Thingy:53 to demonstrate a solution.
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Expert Network Project
Health/Wearables
Flawless Defect Detection with Edge Impulse and BrainChip
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Blog
Industrial
No Scalpel Left Behind: Tracking Surgical Tools with AI and Synthetic Data from NIVIDA Omniverse
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Blog
Computer Vision
Pump Up the Predictions: Prototyping Predictive Maintenance with RASynBoard
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Expert Network Project
Predictive Maintenance
Giving AI X-Ray Vision
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Blog
Health/Wearables
VeLO: Cycling Over the Slopes of Neural Networks Optimization
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Blog
Other
Icicle Detection with NVIDIA Omniverse - Cracking a Cold Case with Synthetic Data
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Expert Network Project
Computer Vision
Introducing EON Compiler (RAM Optimized)
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Blog
Other
Announcing New Integrations: Edge Impulse Optimizes NVIDIA AI for the Edge
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Blog
Computer Vision
Picking the Brain of AI: BrainChip on the State of Neuromorphic Processing
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Blog
Other
Tech for Good: Using AI to Track Rhinos in Africa
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Blog
Conservation
Elevate Your Inspection Tasks with Viusal Anomaly Detection
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Blog
Computer Vision
Introduction to Edge AI Course
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Video
Other
Unlocking the Power of Edge AI for Business Agility and Innovation
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Blog
Other
Bringing Large Language Models (LLMs) to the Edge with GPT-4o
Here's how to use an LLM's understanding to train a much smaller model that can run rapidly and accurately direct on an MCU.
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