When you look at the topics that garner the biggest spotlight these days, AI at the Edge is either at the top of the list or very close. As more processing power becomes available, the applications that can take advantage of this technology grow in leaps and bounds.
Upon visiting the STMicroelectronics booth at the recent embedded world trade show, I stumbled across some interesting software aimed at developing your own machine-learning applications.
When it comes to embedded computers for industrial applications, size does matter—sometimes.
Processors to handle AI at the Edge are quite complex. However, that hasn’t stopped so many companies from producing their variants.
I have done lots of these Embedded Executive podcasts, a few hundred in fact. This particular podcast goes in a direction I have never gone before, and involves the mafia (yes, you read that correctly).
Based on its shiny new RISC-V MCU that’s now ready for prime time, the OpenHW Group is releasing a development kit so developers can get started on their “open hardware” projects, particularly those that want to connect to the IoT.
For starters, Smith is the largest electronics distributer that I have never heard of. They are a multi-billion-dollar company. One of the strengths of the company is ensuring that its products are real (as opposed to counterfeits).
Here at Embedded Computing Design, we spend a lot of time helping engineers/developers work through the pains of industrial designs.
I’m a Visual Studio Code (VS Code) user. If you’re not familiar with VS Code, it’s a source-code editor from Microsoft that is intended for systems based on Windows or Linux. It’s mostly used for debugging, but has lots of other great features. Oh, did I mention that it’s open source, aka free? Yup, the price is right.
Richard Lind is about seven months into his role as CEO of IAR Systems, one of the leaders in embedded development tools.
If you’re designing an IoT product, pretty much any IoT product, NXP has the processor for you.
United SiC, now a division of Qorvo, recently announced a next-generation series of 1200-V Silicon Carbide (SiC) FETs with what the company claims is the industry’s best spec for on-resistance.
Embedded Executive: Jeffrey Morroni, Dir. of Power Management R&D for Kilby Labs, Texas Instruments - PodcastJune 08, 2022
Texas Instruments is a big company. One thing that’s afforded by a company of its size is a lab that can help the various product groups on their designs. And that’s the role of Kilby Labs at TI.
Sensor Fusion—it sounds like something out of Star Trek, like some super-futuristic technology. In reality, it’s simply combining sensors in one device. While it sounds simple, it’s actually fairly difficult to do, as different sensor types often employ different technology and have different outputs, and obviously different input types.
Artificial intelligence is all the rage. Combine that with the huge amount of video that’s being captured. When you put these two phenomena together, you can (potentially) do some great things.
This week’s Road to Embedded World takes a different twist, in that we are previewing an event that’ll take place on site—but you needn’t be in Nuremberg, Germany to witness it. In fact, it’s designed for engineers who are not on site, and produced at a time that’s easily digestible for people in North America.
The chip shortage and supply-chain issues are not getting better.
AI & Machine Learning
Machine vision is the concept of using cameras as an aid in industrial applications, including factory automation. “Aid” can mean different things to different people. For example, if you are responsible for product quality on an assembly line, you can use machine vision to inspect each product as it rolls off the line to ensure that it is being manufacturing to the required specifications and that there are no defects.