Embedded Computing Design

Quantum computing can be a very complex area. Many experts will tell you that it could be as long as a decade before quantum computers actually appear on the scenes.
 

If you’re a consumer, 5G is likely in your universe. If you’re working on the industrial side, you’re likely not seeing 5G at all. And it may be many years before it’s even available in limited deployments. Why is that? That’s the question I asked of Olivier Pauzet, the EVP of Strategy for Sequans Communications in this week’s Embedded Executives podcast.

Are you wondering what would be considered a practical application for AI at the endpoint (the place where the data is captured)? Well, you’re in luck, because Rich is moderating a panel at Renesas’ AI Live virtual conference that specifically covers that topic. 
 

Sustainability continues to be a topic of interest. First, what does sustainability actually mean? Second, what are some of the industry giants doing about it?
 

In the first of this on-going blog series, we explained why a developer should consider implementing Windows on Arm (WoA), and in Part II, we talked about why you should be implementing Windows on Arm using Advantech hardware. Here in Part III, we’ll talk about the steps you should take to actually deploy Windows IoT on Advantech Arm-based hardware.

Bringing all the different elements together for an AI-based hardware platform is harder than it sounds, especially when you start considering the environmental conditions. Getting it to work in the lab is one thing, but the real world presents challenges you may not have thought of.

When it comes to AIoT, some of the terms and techniques you’ll need to familiarize yourself with include Edge computing, machine learning (ML), TinyML, anomaly detection, natural language processing, computer vision, and predictive maintenance.

As you would expect, “Windows on Arm” refers to a version of the Microsoft Windows operating system that’s designed to run on devices powered by Arm-based processors. Such devices are quite plentiful. They are in most of our smartphones, tablets, and a slew of other low-cost devices, many of them powered by batteries.

There are better ways to write embedded code than the traditional methods. Doing it in a visual fashion is one of those ways. But how does that work, and how does it adapt specifically to my hardware?

It seems like more and more engineers are graduating from universities, yet we still need more. There are a few reasons for this.
 

ESG stands for environmental, social, and governance, and it’s something we are hearing a lot about lately. However, in my role as our industry’s devil’s advocate, I want to know if this is a real thing, or just a company line from some of the players in our industry.
 

Seemingly, if you've read any news report that touches on the technology sector, there’s some mention of artificial intelligence. But too often, we hear people talking about AI as an application, and that’s wrong, just like the IoT is not an application.

Welcome to Part II of this two-part blog. In Part I, I explained why AI is the enabler to an application, and not an application unto itself. This is a fairly common misconception, especially as AI is such a new technology.

Speech recognition on an FPGA? That doesn’t sound like the most effective path, but Bill Jenkins, of Achronix had a different opinion. Hear his take on why the FPGA is the right way to go for this application on this week’s Embedded Executives podcast.

We say all the time that any system can be hacked and this week’s Embedded Executives podcast guest is someone who can get into your system.
 

Having a standard that everyone can design to makes markets take off. Open source, both hardware and software, is nothing new. But when paired with AI, it is relatively new.
 

I’ve said it before and I’ll say it again—John Deere is a technology leader that sells agriculture equipment, not the other way around.

Gallium nitride technology has been in the news quite a lot over past couple of years. It’s being touted as a far superior alternative to some of the competing technologies, particularly those that are silicon-based.
 

Whether you’re aware of it or not. AI is pervading many of our “traditional” applications, such as HVAC.

If you took a general poll in the embedded space and asked, “What’s currently the hottest application,” a majority of the responses would be AI. However, that response would be inaccurate because AI (or its subset machine learning) is not an application. It may be the means to some other application, but on its own, it’s a technology.