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Is this ‘just’ using a set of sensor data as the input to a classifier? Or am I missing something more complex (and if so, could someone explain it?). If it’s ‘

by c54 6y ago

Is this ‘just’ using a set of sensor data as the input to a classifier? Or am I missing something more complex (and if so, could someone explain it?). If it’s ‘just’ the former, then why hasn’t this been done before? (‘just’ in quotes bc I know the word always reduces a whole world of complexity)

datameta 6y ago
Simply put miniaturization of neural networks coupled with more powerful yet more energy efficient microcontrollers has allowed projects like this to be feasible since '19, maybe '18. In retrospect it seems an obvious next step from edge ML but at one point it looked like ML was only possible in the domain of beefy GPUs or power hungry CPUs on the cloud. A cornerstone of embedded ML aka TinyML is that not sending data using comms decreases energy consumption by many magnitudes when compared to chip ops, SRAM use, sensor I/O etc. What we have is the culmination of many techniques applied in concert. A huge one is float32 -> int8 quantization to compress the network 4x with usually only around ~1-2% drop in accuracy. I for one am incredibly excited about the prospects of this nascent field.
- 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 ago
    Yes, 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 ago
      Understood. 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.

by c54 6y ago

datameta 6y ago

Simply put miniaturization of neural networks coupled with more powerful yet more energy efficient microcontrollers has allowed projects like this to be feasible since '19, maybe '18. In retrospect it seems an obvious next step from edge ML but at one point it looked like ML was only possible in the domain of beefy GPUs or power hungry CPUs on the cloud. A cornerstone of embedded ML aka TinyML is that not sending data using comms decreases energy consumption by many magnitudes when compared to chip ops, SRAM use, sensor I/O etc. What we have is the culmination of many techniques applied in concert. A huge one is float32 -> int8 quantization to compress the network 4x with usually only around ~1-2% drop in accuracy. I for one am incredibly excited about the prospects of this nascent field.

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 ago
  Yes, 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 ago
    Understood. 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.