When a company like Nvidia invests heavily in the Industrial Internet of Things market like it has with the introduction of the Jetson brand of embedded processors, it turns a lot of people's heads. But will the company have the same type of impact it has had on industrial automation that it has on the datacenter universe by offering endless amounts of GPU horsepower to tackle the inferencing, machine language and computer vision applications installed on assembly lines? The smart money says eventually but it will take a while.
Nvidia has tried to demonstrate its commitment to the industrial embedded market and one of its goals is to revolutionize edge processing tasks for Artificial Intelligence in factories to make assembly lines more productive through improved quality control, predictive maintenance, autonomous vehicle operation and visual inspection among other things. Some industry watchers say generative AI is now gaining momentum in the industrial automation space.
But can you just imagine how Nvidia's overtures are going over with the people who operate assembly lines? Anecdotal evidence supports the theory that manufacturing technology adoption for industrial users occurs at a much slower pace than it does for datacenter users. Factory tech personnel eschew pie-in-the-sky productivity promises that leading-edge technology offers because they place more weight on safety, security, reliability and 24-hour uptime. New technology is often perceived as unnecessarily disruptive in a manufacturing environment, and it poses risk to the continuous flow of operations because it introduces challenges.
A deeper dive into the innovative technology that IIOT providers are promising for factories reveals some of the end-user benefits that may stir demand. Perhaps a small handful of industries will adopt the latest and greatest as soon as they are available, but every environment is different and so actual results may vary.
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Denne historien er fra September 2024-utgaven av Circuit Cellar.
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Bringing Cost-Effective, On-Device AI to Home Appliances - Sparse Weights and Interactions Negate GPUs and Cloud Computing
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After exploring low-level layers of Bluetooth Low Energy in previous editions, and establishing connections in Part 4, Robert examines Attribute Protocol and Generic Attribute Profile or GATT in Part 5. These higher layer protocols provide developers with greater opportunity for interoperability with third-party applications or products.
Retro-style Miniature Sports Scoreboard - RGB LED Matrix and Raspberry Pi Provide In-Game Updates
This Cornell University graduate describes how he built a miniature, retrostyle scoreboard that pulls and displays live college and professional sports scores from the ESPN API-bringing the excitement of being at a sporting event to your living room. Pre-game information, in-game scores, and post-game results are displayed on a built-in PiTFT touchscreen.
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Inspired by the possibility of building an early warning system for tornadoes, Dev developed an infrasound detector. Tornadoes are rare in his area, but his detector found other infrasound activity using sensor circuitry based on a Raspberry Pi Pico, combined with a Python supervisory program that stores data on a computer for analysis.
