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To be clear this tool (ALFaLDS) isn't that. The model runs on a full-fledged system, or as full-fledged as a laptop might seem compared to a microcontroller. It
by fl0wenol 6y ago
To be clear this tool (ALFaLDS) isn't that. The model runs on a full-fledged system, or as full-fledged as a laptop might seem compared to a microcontroller. It integrates with many type of gas and wind sensors which are the dispersed and/or low powered piece.
- datameta 6y agoYes, I am aware from my brief read of the paper. I took the logical leap into touching upon embedded ML. I should point out that the current achievable complexity of embedded neural networks doesn't at the moment remove edge ML from being the method of choice where on-device inferencing isn't applicable. Edge ML is already alleviating much backhaul traffic otherwise generated by cloud inferencing. There are many scenarios where the latency introduced by transmission over network to cloud servers is prohibitive. Also where power contraints are not applicable nor remote operation absolutely vital (in the forest, as an animal tag, on oil platforms, etc) an edge hub to the sensor spokes is the way to approach a problem.
- fl0wenol 6y agoUnderstood. I guess I didn't make that leap because I find that embedded ML isn't what I imagined in this type of scenario. I would assume you'd invest more heavily in power usage to make the mesh sensor network fast/resilient. A "base station" with connectivity for alerting and utility power at the edge of the sensor network is where I imagine any ML to run, and that could power your typical CUDA-capable GPU if necessary. I guess that's still edge ML if the point is to not backhaul everything to a datacenter somewhere, but I wouldn't call it embedded. That brought mind low powered devices with many sensors or sensors that capture more local data (i.e. microphones, cameras) vs. just an anemometer, GPS, and chemical ppm.