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When running Edge on the target platform isnвЂ™t feasible, we can simply export our models to the Crystal format, which lets us perform much of the same machine learning magic on much more constrained hardware, requiring only a C toolchain.Д¬oming back to our Commodore 64 challenge, I will highlight a couple of things regarding the hardware itself, as well as the surrounding software development support. Crystal вЂ“ which will be our main focus for the remainder of this post вЂ“ acts as EdgeвЂ™s smaller sibling. Primer is a closely-tied companion to Edge, providing further functionality aimed at engineers and data scientists developing machine learning solutions intended to be deployed using Edge, such as data augmentation and automated machine learning (AutoML) tools. Its primary use is to serve as a machine learning backbone for smart applications running on relatively capable systems with a mature C++ toolchain (e.g., everything from a Raspberry Pi to a cloud-hosted server).

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Edge is the core of our software offering, providing our full suite of machine learning algorithms, data pre-processing pipelines, and model evaluation tools. Our machine learning library suite is (at current) implemented and distributed as three separate-but-integrated subsystems: Edge, Primer, and Crystal. However, before I get into the details of running our machine learning algorithms on a C64, I want to offer a bit of context.

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So, in order to illustrate (and show off, a little bit) just how portable EkkonoвЂ™s machine learning SDK really is, I set myself a challenge: to get our stuff running on the most ubiquitous computer of all time (according to Guiness World Records), the Commodore 64. While ensuring that our software library delivers a high degree of portability is very important to us, it doesnвЂ™t necessarily sound particularly exciting, or even impressive, if your world is not centered around working with machine learning algorithms on constrained hardware.

Owning every single line of code that goes into our compiled libraries means weвЂ™re able to ensure a high degree of compatibility across hardware platforms and compiler toolchains. One such consideration is our purposeful decision not to rely on any third-party code (whether that is open or closed source) in our SDK. The ability to deploy, and run, our software library on constrained hardware platforms hinges on a number of considerations related to our R&D process, from research and algorithm design, to implementation and testing.

Here, we will elaborate further on two of the design principles Eva outlined in her post: ease of deployment and, perhaps more excitingly, вЂњCan it run anywhere?вЂќ. Back in June, one of our data scientists, Eva Garcia Martin, wrote about some of the challenges of designing and building machine learning software for edge devices, and how those challenges influence our R&D processes. The potential benefits of analyzing data close to the source, rather than uploading it to the cloud, are many: faster response times, increased security and privacy, and improved energy efficiency, to name a few. At Ekkono, weвЂ™re all about pushing smart functionality as far out on the network edge as possible.

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