Edge Impulse
Assembly Line
CELUS Partners with Edge Impulse to Revolutionize AI-Driven Electronics Design
CELUS, the leading provider of AI-powered electronics design automation solutions for developers, engineers, and component suppliers, and Edge Impulse, the leading platform for building, optimizing and deploying machine learning models and algorithms to edge devices, announced a strategic partnership aimed at transforming the landscape of electronics design and development.
The collaboration brings together the AI-assisted, CELUS Design Platform with Edge Impulse’s advanced ML development platform. The combination of the two companies’ technologies streamlines the design process, enhances efficiencies, and accelerates the time to market of innovative electronic products.
Easy Data Cleanup with Generative AI
Building an Automated Manufacturing Inspection System with FOMO-AD
Just about anything can potentially go wrong in producing a product, so naive approaches that look for specific defects quickly become impractical. For this reason, Bandini decided to put Edge Impulse’s new FOMO-AD algorithm to work. FOMO-AD utilizes a Gaussian mixture model to detect anomalies in conjunction with the powerful and highly efficient FOMO object detection algorithm. This approach allows one to train the model on only normal instances of an object, after which it will be able to recognize any deviations from that normal state. Furthermore, FOMO-AD can pinpoint the locations in an image where anomalies exist, making the inspection process as painless as possible.
Computer vision algorithms tend to be very expensive computationally, but due to the efficiency of the FOMO-AD model, Bandini was able to easily run it on edge computing hardware to keep costs and latency down. In this case, he selected the Texas Instruments SK-TDA4VM development kit. The onboard TDA4VM processor offers eight trillion operations per second of hardware-accelerated AI processing power, which is well more than what is required for the project. Yet the SK-TDA4VM is also inexpensive and requires little power for operation, making it suitable for large-scale deployments. He then paired the kit with a USB webcam to allow it to capture images of components for anomaly detection.
How to Train an Object Detection Model for Visual Inspection with Synthetic Data
Edge Impulse is an integrated development platform that empowers developers to create and deploy AI models for edge devices. It supports data collection, preprocessing, model training, and deployment, helping users integrate AI capabilities into their applications effectively.
With NVIDIA Omniverse Replicator, a core extension of NVIDIA Omniverse, users can produce physically accurate and photorealistic, synthetically generated annotated images in Universal Scene Description, known as OpenUSD. These images can then be used for training an object detection model on the Edge Impulse platform.
Taking a data-centric approach, where you create more data around the failure points of the model, is crucial to solving ML problems. Additional training and fine-tuning of parameters can enable a model to generalize well across different orientations, materials, and other relevant conditions.