sysprogs

Even if you get the perfect training data, the state-of-the art neural networks will not be 100% reliable. Convolutional neural networks work by recursively recognizing shapes. In theory, the first layer should match the picture on something like this and conclude "it's probably an eye":

  o o X o o
  o X X X o
  o X X X o
  o o X o o

(above, "X" denotes low relative brightness, "o" denotes high relative brightness).

The next convolutional layer is supposed to see something like this and conclude it's a face:

  (eye)        (not a bumper)    (eye)
  (not a door)    (nose)     (not a wheel)
  (not a shovel) (mouth)   (not a window)

That's in theory. In practice, each input to the second layer will be a vector of probabilities. Like 90% eye, 80% wheel, 1% door. The output will also look like a vector of probabilities, and the layer is supposed to figure out a linear formula matching inputs to outputs that results in the best statistical outcome. So it can very easily come up with kernels like this:

* A: (an eye or a wheel) and maybe a bumper to the right of it

* B: (an eye or a wheel) and maybe a bumper to the left of it

* C: (two wheels) side-by-side

In this case, the baby face will be encoded as A together with B and definitely not C. Because each next level produces a linear combination of the previous level's outputs, the "or a wheel" parts will get nicely canceled out as long as the "2 wheels" kernel input is a low value, concluding that it's a face with a confidence of 99%. Until the light falls from a different angle and the baby holds a round lego brick near their face, suddenly yielding 80% confidence that it's a car.

Drawing parallels to organic life, neural networks are good at replicating inborn instincts. Like animals instantly recognizing predators, food or poisonous plants. But they are still far away from having a situational awareness needed to make judgement calls in most real-world situations.

Sure, an FPGA does the job faster, but how popular are the FPGA boards compared to something like STM32 Nucleo? What we are trying to achieve is make a software logic analyzer that will work on top of the hardware that everyone has for prototyping anyway. 8 boards is just the beginning...

And yes, USB3.0 Cypress FX3 is in our short list.

Oh yeah, we looked into the open-source ones. The main problems there were irregular sampling and software that will make you sweat before you get your first reliable measurement. So we wanted to create something that will be extremely easy to use and will reliably work out-of-the-box.