In the same way "embedded" is relative, I appreciate the author's recognition that "edge" is relative. For some, AI at the edge means on-prem server farms. For some it means a mini-pc. For others, maybe an SBC. Here it's a microcontroller. Further still is AI within the sensors a microcontroller would talk to. That's probably just another microcontroller but still.
There's "micro" and "micro". The microcontroller operating a simple coffee machine, or a simple washing-machine is probably 8 or 16 bits. This is what I would call "bare metal", as they don't run an OS, only off-the-shelf frameworks at best.
For "bigger" devices, it's usually a Cortex inside a system-on-chip or system-on-module, 32 bits single core and a few Mb of RAM for low-end (enough to run regular Linux distro instead of uClinux for instance), 64 bits multicore for high-end devices that deal with audio/video. That kind of business is often resource-hungry in every way.
I work with that kind of stuff, and to me these "microcontrollers" are just monsters that I hesitate to call "micro" when some of my coworkers work on much smaller chips with only a few K of RAM available.
tf-lite micro library has many advantages, and the first of these is the tensorFlow framework itself. You can train the model easily and then implement the same or a similar architecture on esp-32s without much effort. Another advantage is its optimization and you can easily intervene in various memory optimizations and even though it is not a large one, it does have a community.
Apart from these, for example, the author implemented the model the traditional way using C, but it is more convenient to use tf-lite micro on esp32s with the Berry script language.
However, since I have never used onnxin this kind of project, I cant speak to its advantages, so comparisons are difficult from my perspective. But as I said, tf-lite and offer benefits like easy integration, good optimization, and as the name implies, tensorFlow.
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For "bigger" devices, it's usually a Cortex inside a system-on-chip or system-on-module, 32 bits single core and a few Mb of RAM for low-end (enough to run regular Linux distro instead of uClinux for instance), 64 bits multicore for high-end devices that deal with audio/video. That kind of business is often resource-hungry in every way.
I work with that kind of stuff, and to me these "microcontrollers" are just monsters that I hesitate to call "micro" when some of my coworkers work on much smaller chips with only a few K of RAM available.
Wouldn't it be advantageous if we used ONNX for everything? https://onnx.ai/
Apart from these, for example, the author implemented the model the traditional way using C, but it is more convenient to use tf-lite micro on esp32s with the Berry script language.
However, since I have never used onnxin this kind of project, I cant speak to its advantages, so comparisons are difficult from my perspective. But as I said, tf-lite and offer benefits like easy integration, good optimization, and as the name implies, tensorFlow.