The Future of Embedded Computing

Although my academic background is in cybernetics and artificial intelligence, and my career started out in production software development, I have been lucky enough to spend the last few years diving head first into embedded systems development. There have been some amazing steps forward in embedded computing in recent years, and I’d like to share with you some of my favorite recent advances, and how I think they will progress.

While ever-decreasing costs of embedded computing hardware is expected and not too exciting, I think there have been a few key price points that are an indicator of things to come. In the last few months, we have seen the release of Application Processor development boards that are below $10. Tiny gigahertz-level processors that are Linux-ready for an amazingly low price. The most well-known is the Raspberry Pi Zero, which is created by the Raspberry Pi Foundation, who I believe will continue to push this impressive level of development capability into schools, really giving the next generation of engineers (and non-engineers) some hands-on experience. Perhaps a less well known release is C.H.I.P, the new development platform from Next Thing Co. The hardware is like the Pi Zero, but the drive behind the company is quite different. We’ll discuss this more later.

While the hobbyist side of embedded computing is not new, the communities and resources that are being built are exciting. Most of you will have heard of Arduino and Raspberry Pi. The Pi is a low-cost, easy-to-use Linux computer. Arduino is an open-source platform consisting of a super-simple IDE, tons of libraries, and a huge range of development boards. These have set a standard for member of the maker community who expect affordable hardware, open-source designs, and strong community support, and some companies are stepping up to this.

Next Thing Co. has the goal of creating things to inspire creativity. In addition to developing low-cost hardware, they try to remove the pain from the design process and only open-source, well-documented products will do. This ethos is embodied in their C.H.I.P Pro, which is not just an open-source Linux System-on-Module. It’s built around their own GR8 IC, which contains an Allwinner 1-GHz ARM Cortex-A8, as well as 256 MB of DDR3 built in, accompanied with an open datasheet requiring no NDA, and with a one-unit minimum order quantity. This really eliminates the headaches of high-speed routing between DDR3 and the processor, and it reduces the manufacturing complexities of creating a custom Linux ready PCB. Innovation and progress like this provide a lot more value than the many other companies just producing insufficiently documented breakout boards for existing chips. I think that this will be a company to watch, and I can’t wait to see what their next ambitious project will be.

We’ve all been witnessing the ever-increasing performance of embedded systems, as successive generations of smart phones and tablets are released, but when I talk about high performance I don’t refer to a measly 2+GHz Octa-core system with a few Gig of RAM, I’m talking about embedded supercomputing!

As far as I’m concerned, the one to watch here is NVIDIA. Their recent Tegra series sees them bringing massively parallel GPU processing to affordable embedded devices. The Tegra 4 had a quadcore CPU and 72 GPU cores. The TK1 has a quadcore CPU and 192 GPU cores, and the most recent TX1 has an octacore CPU and a 256 GPU cores that provide over 1 Teraflops of processing power. These existing devices are very impressive, but NVIDIA are not slowing down development, with the Xavier expected to appear at the end of 2017. Boasting 512 GPU cores and a custom octacore CPU architecture, the Xavier claims to deliver 20 trillion operations per second for only 20-W power consumption.

NVIDIA is developing these systems with the intent for them to enable embedded artificial intelligence (AI) with a focus on autonomous vehicles and real-time computer vision. This is an amazing goal, as AI has historically lacked the processing power to make it practical in many applications, and I’m hoping that NVIDIA is putting an end to that. In addition to their extremely capable hardware, they are providing great software resources and support for developing deep learning systems.

We are on the horizon of some exciting advancements in the field of embedded computing. In addition to seeing an ever-growing number of IoT and smart devices, I believe that during the next few years we’ll see embedded computing enable great advancements in AI and smart cities. Backyard developers will be enabled to create more impressive and advanced systems, and technical literacy will become more widespread.

This essay appears in Circuit Cellar 321.


Steve Samuels ( is a Cofounder and Prototype Engineer at Think Engineer LLP, a research, development and prototyping company that specializes in creating full system prototypes and proof-of-concepts for next-generation products and services. Steve has spent most of his career in commercial research and development in domains such as transportation, satellite communications, and space robotics. Having worked in a lot of different technical areas, his main technical interests are embedded systems and machine learning.