feature article
Subscribe Now

Machine Learning at the Push of a Button

Automated Tool Generates ML Code for IoT Devices

“Physician, heal thyself” – Luke 4:23

My Thermos bottle keeps hot drinks hot and cold drinks cold. How does it know? 

An electrical engineer would probably design a Thermos with a toggle switch (“HOT” and “COLD”), or a big temperature dial, or – if you work in Cupertino – an LCD display, touchpad, RTOS, and proprietary cable interface. Thankfully, real vacuum flasks take care of themselves with no user input at all. They just work. 

It would sure be nice if new AI-enabled IoT devices could do the same thing. Instead of learning all about AI and ML (and the differences between the two), and learning how to code neural nets, and how to train them, and what type of data they require, and how to provision the hardware, etc., it’d be great if they just… somehow… knew what to do. Now that would be real machine learning. 

Guess what? A small French company thinks it has developed that very trick. It uses machine learning to teach machine learning. To machines. Without a lot of user input. It takes the mystery, mastery, and mythology out of ML, while allowing engineers and programmers to create smart devices with little or no training.  

The company is Cartesiam and the product is called NanoEdge AI Studio. It’s a software-only tool that cranks out learning and inference code for ARM Cortex-M–based devices, sort of like an IDE for ML. The user interface is pretty to look at and has only a few virtual knobs and dials that you get to twist. All the rest is automatic. Under the right circumstances, it’s even free. 

Cartesiam’s thesis is that ML is hard, and that developing embedded AI requires special skills that most of us don’t have. You could hire a qualified data scientist to analyze your system and develop a good model, but such specialists are hard to find and expensive when they’re available. Plus, your new hire will probably need a year or so to complete their analysis – and that’s before you start coding or even know what sort of hardware you’ll need. 

Instead, Cartesiam figures that most smart IoT devices have certain things in common and don’t need their own full-time, dedicated data scientist to figure things out, just like you don’t need a compiler expert to write C code or a physicist to draw a schematic. Let the tool do the work. 

The company uses preventive motor maintenance as an example. Say you want to predict when a motor will wear out and fail. You could simply schedule replacement every few thousand hours (the equivalent of a regular 5000-mile oil change in your car), or you could be smart and instrument the motor and try to sense impending failures. But what sensors would you use, and how exactly would they detect a failure? What does a motor failure look like, anyway? 

With NanoEdge AI Studio, you give it some samples of good data and some samples of bad data, and let it learn the difference. It then builds a model based on your criteria and emits code that you link into your system. Done. 

You get to tweak the knobs for MCU type, RAM size, and type of sensor.  In this case, a vibration sensor/accelerometer would be appropriate, and the data samples can be gathered in real-time or canned; it doesn’t matter. You can also dial-in the level of accuracy and the level of confidence in the model. These last two trade off precision for memory footprint.  

NanoEdge Studio includes a software simulator, so you can test out your code without burning any ROMs or downloading to a prototype board. That should make it quicker to test out various inference models to get the right balance. Cartesiam says it can produce more than 500 million different ML libraries, so it’s not simply a cut-and-paste tool. 

As another example, Cartesiam described one customer designing a safety alarm for swimming pools. They spent days tossing small children into variously shaped pools to collect data, and then several months analyzing the data to tease out the distinguishing characteristics of a “good” splash versus one that should trigger the alarm. NanoEdge AI Studio accomplished the latter task in minutes and was just as accurate. Yet another customer uses it to detect when a vacuum cleaner bag needs emptying. Such is the world of smart device design. 

The overarching theme here is that users don’t have to know much of anything about machine learning, neural nets, inference, and other arcana. Just throw data at it and let the tool figure it out. Like any EDA tool, it trades abstraction for productivity. 

In today’s environment, that’s a good tradeoff. Experienced data scientists are few and far between. Moreover, you probably won’t need his/her talents long-term. When the project is complete and you’ve got your detailed model, what then? 

NanoEdge AI Studio is free to try but deploying actual code in production costs money. Cartesiam describes the royalty as “tens of cents to a few dollars,” depending on volume. Sounds cheaper than hiring an ML specialist. 

One thought on “Machine Learning at the Push of a Button”

Leave a Reply

featured blogs
May 24, 2022
By Melika Roshandell Today's modern electronic designs require ever more functionality and performance to meet consumer demand. These requirements make scaling traditional, flat, 2D-ICs very... ...
May 24, 2022
Nicholas Temese, who hails from Quebec, Canada, creates highly detailed handcrafted miniature scale models of classic computers from yesteryear....
May 24, 2022
By Neel Natekar Radio frequency (RF) circuitry is an essential component of many of the critical applications we now rely… ...
May 19, 2022
Learn about the AI chip design breakthroughs and case studies discussed at SNUG Silicon Valley 2022, including autonomous PPA optimization using DSO.ai. The post Key Highlights from SNUG 2022: AI Is Fast Forwarding Chip Design appeared first on From Silicon To Software....

featured video

EdgeQ Creates Big Connections with a Small Chip

Sponsored by Cadence Design Systems

Find out how EdgeQ delivered the world’s first 5G base station on a chip using Cadence’s logic simulation, digital implementation, timing and power signoff, synthesis, and physical verification signoff tools.

Click here for more information

featured paper

Intel Agilex FPGAs Deliver Game-Changing Flexibility & Agility for the Data-Centric World

Sponsored by Intel

The new Intel® Agilex™ FPGA is more than the latest programmable logic offering—it brings together revolutionary innovation in multiple areas of Intel technology leadership to create new opportunities to derive value and meaning from this transformation from edge to data center. Want to know more? Start with this white paper.

Click to read more

featured chalk talk

Twinax Flyover Systems for Next Gen Speeds

Sponsored by Samtec

As the demand for higher and higher speed connectivity increases, we need to look at our interconnect solutions to help solve the design requirements inherent with these kinds of designs. In this episode of Chalk Talk, Amelia Dalton and Matthew Burns from Samtec discuss how Samtec’s Flyover technology is helping solve our high speed connectivity needs. They take closer look at how Samtec’s Flyover technology helps solve the issue with PCB reach, the details of FLYOVER® QSFP SYSTEM, and how this cost effective, high–performance and heat efficient can help you with the challenges of your 56 Gbps bandwidths and beyond design.

Click here for more information about Twinax Flyover® Systems for Next Gen Speeds