dansitu

That's definitely not our intention: the output of all Community projects is by default Apache 2.0 licensed, unless the developer specifies a different one.

The community plan does have commercial use restrictions; it's designed for education, demos, and research. We have a pretty good presence in the academic community with tons of papers, code, and projects developed using our community version.

Here's a Google Scholar search showing a bunch of papers:

https://scholar.google.com/scholar?start=0&q=%22edge+impulse...

We also have our own public sharing platform:

https://edgeimpulse.com/projects/overview

We use C++11 because we depend on some source that relies on it, but you can of course compile the library and then link to a C program:

https://docs.edgeimpulse.com/docs/run-inference/cpp-library/...

Thank you! We're trying to bring embedded ML in reach of all engineering teams and domain experts.

Previously you needed a crazy mixture of ML knowledge and low-level embedded engineering skills even to get started, which is not a common occurrence!

If you're interested in this stuff and wanna try it yourself, check out our product, Edge Impulse:

https://edgeimpulse.com/ai-practitioners

We work directly with vendors to perform low level optimization of deep learning, computer vision, and DSP workloads for dozens of architectures of microcontrollers and CPUs, plus exotic accelerators (neuromorphic compute!) and edge GPUs. This includes ESP32:

https://docs.edgeimpulse.com/docs/edge-ai-hardware/mcu/espre...

You can upload a TensorFlow, PyTorch, or JAX model and receive an optimized C++ library direct from your notebook in a couple lines of Python. It's honestly pretty amazing.

And we also have a full Studio for training models, including architectures we've designed specifically to run well on various embedded hardware, plus hardware-aware hyperparameter optimization that will find the best model to fit your target device (in terms of latency and memory use).

It's a great feeling to see a cool robotics project on the front page of Hacker News and realize they used your software (http://edgeimpulse.com) to train the machine learning component!

Congratulations to the authors. Now I better stop reading HN and get back to work :D

It's cool to hear from someone with experience!

Do you know if anyone has tried building an arm that uses spatial positioning techniques from augmented reality, like structured light or pose tracking[1], to understand the position of the arm in space without resorting to "dead reckoning"?

It seems like that kind of approach would increase the physical tolerance and reduce the programming complexity, since you know both a) where the arm is supposed to be, and b) where it actually is.

[1] https://en.wikipedia.org/wiki/Pose_tracking#Outside-in_track...

If you find this field interesting, Know Labs is working on a non-invasive continuous glucose monitor using RF spectroscopy:

https://www.knowlabs.co

Edge Impulse | Full-time | REMOTE | North America & Europe | https://jobs.lever.co/edge-impulse

Edge Impulse is the enterprise platform for edge AI and TinyML; we make it easy to train and deploy machine learning for embedded applications. Our customers are mainly in the industrial, healthcare, and wearables spaces.

It's the best place I have ever worked. Our team includes several pioneers of edge AI. The company is a mix of the best parts of US and European culture, and are super open and transparent.

We are hiring a Technical Product Manager, Senior Full Stack Engineer, Customer Solutions Engineer, Solutions Engineering Manager, Sales Account Executive, and SDRs.

Thank you, I ran out of time to edit but have posted a reply with fixed links :)

Non-broken versions of the links:

* Harvard TinyML course: https://www.edx.org/learn/machine-learning/harvard-universit...

* Coursera intro to embedded ML: https://www.coursera.org/learn/introduction-to-embedded-mach...

* TinyML (my original book, on the absolute basics. getting a bit out of date, contact me if you wanna help update it): https://tinymlbook.com

* AI at the Edge (my second book, focused on workflows for building real products): https://www.amazon.com/AI-Edge-Real-World-Problems-Embedded/...

* ML systems with TinyML (wiki book by my friend Prof. Vijay Reddi at Harvard): https://harvard-edge.github.io/cs249r_book/

* TinyML conference: https://www.tinyml.org/event/summit-2024/

* I also write a newsletter about this stuff, and the implications it has for human computer interaction: https://dansitu.substack.com

There are actually tons of chips that are great for this type of workload. You can run simple vision applications on any 32 bit MCU with ~256kb RAM and ROM.

There's a list of MCUs here:

https://docs.edgeimpulse.com/docs/development-platforms/offi...

And some accelerators here:

https://docs.edgeimpulse.com/docs/development-platforms/offi...

This is just stuff that has support in Edge Impulse, but there are many other chips too.

I'm pretty stoked to see our field at the top of HN, I hope some folks who are reading this end up feeling the spark and getting involved!

It's mostly inference: typically on-device training is with classical ML, not deep learning, so no on-device backprop.

For inference there's a whole spectrum of approaches that let you can trade off flexibility for performance. TF Lite Micro is at one end, hand-written Verilog is at the other.

Typically, flexibility is more important at the start of a project, while deep optimization is more important later. You wanna be able to iterate fast. That said, the flexible approaches are now good enough that you will typically get better ROI from optimizing your model architecture rather than your inference code.

I think the sweet spot today is code-generation, when targeting general purpose cores. There's also increasing numbers of chips with hardware acceleration, which is accessed using a compiler that takes a model architecture as input.

It's great to see TinyML at the top of Hacker News, even if this is not the best resource (unsure how it got so many upvotes)!

TinyML means running machine learning on low power embedded devices, like microcontrollers, with constrained compute and memory. I was supremely lucky in being around for the birth of this stuff: I helped launch TensorFlow Lite for Microcontrollers at Google back in 2019, co-authored the O'Reilly book TinyML (with Pete Warden, who deserves credit more than anyone for making this scene happen) and, ran the initial TinyML meetups at the Google and Qualcomm campuses.

You likely have a TinyML system in your pocket right now: every cellphone has a low power DSP chip running a deep learning model for keyword spotting, so you can say "Hey Google" or "Hey Siri" and have it wake up on-demand without draining your battery. It’s an increasingly pervasive technology.

TinyML is a subset of edge AI, which includes any type of device sitting at the edge of a network. This has grown far beyond the general purpose microcontrollers we were hacking on in the early days: there are now a ton of highly capable devices designed specifically for low power deep learning inference.

It’s astonishing what is possible today: real time computer vision on microcontrollers, on-device speech transcription, denoising and upscaling of digital signals. Generative AI is happening, too, assuming you can find a way to squeeze your models down to size. We are an unsexy field compared to our hype-fueled neighbors, but the entire world is already filling up with this stuff and it’s only the very beginning. Edge AI is being rapidly deployed in a ton of fields: medical sensing, wearables, manufacturing, supply chain, health and safety, wildlife conservation, sports, energy, built environment—we see new applications every day.

This is an unbelievably fascinating area: it’s truly end-to-end, covering an entire landscape from processor design to deep learning architectures, training, and hardware product development. There are a ton of unsolved problems in academic research, practical engineering, and the design of products that make use of these capabilities.

I’ve worked in many different parts of tech industry and this one feels closest to capturing the feeling I’ve read about in books about the early days of hacking with personal computers. It’s fast growing, tons of really hard problems to solve, even more low hanging fruit, and has applications in almost every space.

If you’re interested in getting involved, you can choose your own adventure: learn the basics and start building products, or dive deep and get involved with research. Here are some resources:

* Harvard TinyML course: https://www.edx.org/learn/machine-learning/harvard-universit...

* Coursera intro to embedded ML: https://www.coursera.org/learn/introduction-to-embedded-mach...

* TinyML (my original book, on the absolute basics. getting a bit out of date, contact me if you wanna help update it): https://tinymlbook.com

* AI at the Edge (my second book, focused on workflows for building real products): https://www.amazon.com/AI-Edge-Real-World-Problems-Embedded/...

* ML systems with TinyML (wiki book by my friend Prof. Vijay Reddi at Harvard): https://harvard-edge.github.io/cs249r_book/

* TinyML conference: https://www.tinyml.org/event/summit-2024/

* I also write a newsletter about this stuff, and the implications it has for human computer interaction: https://dansitu.substack.com

I left Google 4 years ago to lead the ML team at Edge Impulse (http://edgeimpulse.com) — we have a whole platform that makes it easy to develop products with edge AI. Drop me an email if you are building a product or looking for work: daniel@edgeimpulse.com