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Arm DevSummit 2020 debuted this week (October 6 – 8) as an online virtual conference focused on engineers and providing them with insights into the Arm ecosystem. The summit lasted three days over which Arm painted an interesting technology story about the current and future state of computing and where developers fit within that story. I’ve been attending Arm Techcon for more than half a decade now (which has become Arm DevSummit) and as I perused content, there were several take-a-ways I noticed for developers working on microcontroller based embedded systems. In this post, we will examine these key take-a-ways and I’ll point you to some of the sessions that I also think may pique your interest.

(For those of you that aren’t yet aware, you can register up until October 21st (for free) and still watch the conferences materials up until November 28th . Click here to register)

Take-A-Way #1 – Expect Big Things from NVIDIAs Acquisition of Arm

As many readers probably already know, NVIDIA is in the process of acquiring Arm. This acquisition has the potential to be one of the focal points that I think will lead to a technological revolution in computing technologies, particularly around artificial intelligence but that will also impact nearly every embedded system at the edge and beyond. While many of us have probably wondered what plans NVIDIA CEO Jensen Huang may have for Arm, the Keynotes for October 6th include a fireside chat between Jensen Huang and Arm CEO Simon Segars. Listening to this conversation is well worth the time and will help give developers some insights into the future but also assurances that the Arm business model will not be dramatically upended.

Take-A-Way #2 – Machine Learning for MCU’s is Accelerating

It is sometimes difficult at a conference to get a feel for what is real and what is a little more smoke and mirrors. Sometimes, announcements are real, but they just take several years to filter their way into the market and affect how developers build systems. Machine learning is one of those technologies that I find there is a lot of interest around but that developers also aren’t quite sure what to do with yet, at least in the microcontroller space. When we hear machine learning, we think artificial intelligence, big datasets and more processing power than will fit on an MCU.

There were several interesting talks at DevSummit around machine learning such as:

Some of these were foundational, providing embedded developers with the fundamentals to get started while others provided hands-on explorations of machine learning with development boards. The take-a-way that I gather here is that the effort to bring machine learning capabilities to microcontrollers so that they can be leveraged in industry use cases is accelerating. Lots of effort is being placed in ML algorithms, tools, frameworks and even the hardware. There were several talks that mentioned Arm’s Cortex-M55 architecture that will include Helium technology to help accelerate machine learning and DSP processing capabilities.

Take-A-Way #3 – The Constant Need for Reinvention

In my last take-a-way, I eluded to the fact that things are accelerating. Acceleration is not just happening though in the technologies that we use to build systems. The very application domain that we can apply these technology domains to is dramatically expanding. Not only can we start to deploy security and ML technologies at the edge but in domains such as space and medical systems. There were several interesting talks about how technologies are being used around the world to solve interesting and unique problems such as protecting vulnerable ecosystems, mapping the sea floor, fighting against diseases and so much more.

By carefully watching and listening, you’ll notice that many speakers have been involved in many different types of products over their careers and that they are constantly having to reinvent their skill sets, capabilities and even their interests! This is what makes working in embedded systems so interesting! It is constantly changing and evolving and as engineers we don’t get to sit idly behind a desk. Just as Arm, NVIDIA and many of the other ecosystem partners and speakers show us, technology is rapidly changing but so are the problem domains that we can apply these technologies to.

Take-A-Way #4 – Mbed and Keil are Evolving

There are also interesting changes coming to the Arm toolchains and tools like Mbed and Keil MDK. In Reinhard Keil’s talk, “Introduction to an Open Approach for Low-Power IoT Development“, developers got an insight into the changes that are coming to Mbed and Keil with the core focus being on IoT development. The talk focused on the endpoint and discussed how Mbed and Keil MDK are being moved to an online platform designed to help developers move through the product development faster from prototyping to production. The Keil Studio Online is currently in early access and will be released early next year.

(If you are interested in endpoints and AI, you might also want to check-out this article on “How Do We Accelerate Endpoint AI Innovation? Put Developers First“)

Conclusions

Arm DevSummit had a lot to offer developers this year and without the need to travel to California to participate. (Although I greatly missed catching up with friends and colleagues in person). If you haven’t already, I would recommend checking out the DevSummit and watching a few of the talks I mentioned. There certainly were a lot more talks and I’m still in the process of sifting through everything. Hopefully there will be a few sessions that will inspire you and give you a feel for where the industry is headed and how you will need to pivot your own skills in the coming years.

Originaly posted here

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Industrial IoT Revolution

Why the Nvidia Jetson Nano is responsible for the biggest industrial IoT revolution these days

 
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It feels like yesterday when the Raspberry Pi foundation released the first-in-line Single Board Computer (SBC) to the market. Back in 2012, Raspberry Pi wasn't alone in the SBC growing market, however, it was the first to make a community-based product that brings the hardware and the software eco-system to a beautiful harmony on the internet. Before those days, embedded Linux based SBC's and SOM's were a place for Linux kernel and embedded hardware experts, no easy-to-use tools, ready Linux based distros, or most importantly without the enormous amount of questions and answers across the internet on anything related.

Today, 8 years later, the "2012 revolution" happens again

This time, it took a year to understand the impact of the new 'kid' in the market, but now, there are a few indications that defiantly build the route to a revolution.

The Raspberry Pi was the first to make embedded Linux easy while keeping the advantages of reliability and flexibility in terms of fitting to different kinds of industries applications. It's almost impossible to ignore the variety of industries where Raspberry Pi is in its hurt of products to save time-to-market and costs. The power of this magical board leans on the software side: The Raspberry Pi foundation and their community, worked hard across the years to improve and share their knowledge, but, at the same time, without notice or targeting, they brought the Pi development to an extremely "serverless" level.

The Nvidia Jetson Nano

Let's stop talking about the Raspberry Pi and focus on today's industry needs to understand better why the new kid in the town is here to change the market of IoT and smart products forever.

 
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 Why do we need to thanks Nvidia and the Jetson Nano?
 

The market is going forward. AI, Robotics, amazing-looking screen app Gui's, image processing, and long data calculations are all become the new standard of smart edge products.

If a few years ago, you would only want to connect your product to the cloud and receive anything valuable, today, product managers and developers compete in a much tougher industry era. This time, the Raspberry Pi can't be the technology hero again, its resources are limited and the eco-system starts to squint to a better-fit solution.

 
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NVIDIA Jetson devices in Upswift.io device management platform

The Jetson Nano is the first SBC to understood the necessary combination that will drive new products to use it. It's the first SBC designed in the mind of industrial powerful use cases, while not forgetting the prototyping stage and the harmony that gave the Raspberry Pi their success. It's the first solution to bring the whole package for developers and for hardware engineers with a "SaaS" feel: The OS is already perfect thanks to Ubuntu, there is plenty of software instructions by Nvidia and open-source ready-to-use tools custom made for the Jetson family, and for the hardware engineers: they are free to go with the System On Module (SOM) that is connected to a carrier board which includes all the necessary outputs and inputs to make the development stage even faster.

The Jetson Nano combination is basically providing the first world infrastructure for producing a "2020" product with complex software while working in a minimal budget and time-to-market. The Jetson Nano enables developers and product managers to imagine further without compromises, bringing tough software missions to the edge easily.

Originally posted here

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Connected Cars: From the Edge to the Cloud

Many of us have yet to see an autonomous vehicle driving down the road, but it will be here faster than we can image. The car of tomorrow is connected, data-rich and autonomous. As 5G networks come online, sensors improve and compute and memory become faster and cheaper, the amount of data a vehicle will generate is expected to be 40 terabytes of data every day. This will make the autonomous vehicle the ultimate edge computing device.

Last week at Mobile World Congress Americas in San Francisco, Micron Technology hosted a panel discussion with automotive industry experts where they discussed the future of the connected car and the role of both the cloud and the edge in delivering the full promise of autonomous driving (FYI – Cars are now big at wireless trade shows. See Connected Vehicle Summit at MWC).

Experts from Micron, NVIDIA, Microsoft and Qualcomm discussed what 5G, cloud, IoT and edge analytics will mean for next-generation compute models and the automobile.

Micron claims to be the #1 memory supplier to the automotive industry and notes that its technology will be required to access the massive streams of data from vehicles. This data must be processed and analyzed, both in the car and in the cloud. Think about going down the road at 70 MPH in an autonomous vehicle. You need to have safe, secure and highly-responsive solutions, relying on split second decisions powered by enormous amounts of data. To quickly analyze the data necessary for future autonomous vehicles, higher bandwidth memory and storage solutions are required.

Smart, connected vehicles are the poster child for edge computing and IoT.

Some intriguing quotes from the discussion:

  • “In last seven years 5839 patents have been granted for autonomous vehicle technology.” – Steve Brown, Moderator and Futurist
  • “There is a proactive side of autonomous driving that can’t be fulfilled at the edge.” Doug Seven, Head of Connected Vehicle Platform, Microsoft
  • “The thin client model won’t work for automobiles. You won’t have connectivity all the time.” Steve Pawlowski, Vice President Advanced Computing Solutions, Micron
  • “Once you have enough autonomous vehicles, the humans are the danger.” Tim Wong, Director of Technical Program Management for Autonomous Vehicles, NVIDIA

The entire panel discussion can be found in the video below.

Disclaimer: The author of this post has a paid consulting relationship with Micron Technology. 

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