Well it is a very simple solver, you can't change it after it's synthesized and it's not true it uses no power - you need strong enough incoming light. That's your power source. It's probably safe to assume that each layer removes some power in a mutiplicative fashion, therefore it's only so much you can do in a single glass step before the beam dissipates.

And as far as timing goes - 1 cm of glass delays by ~50 picoseconds. That corresponds to around 20 gigahertz. Fast, but not mind blowingly fast.

An OCR that could read 20B freehand characters per second using the power of an LED is not mind blowing?

Well it's MNIST - most basic and classic OCR benchmark out there. Also, no info on how much light power is actually necessary, though I'm sure it's exponential in the number of layers. And another thing I forgot to write is: how are you going to interface with this thing?

Nothing traditional. But who knows, there's probably a ceramics researcher somewhere with some new comms or storage technology asking the converse.

Glass is 100% recyclable. It's conceivable that a robot could be built to fabricate ML models and meltdown / update old models when necessary. That would be pretty steampunk.

Engines of Creation might be the book for you: http://xaonon.dyndns.org/misc/engines_of_creation.pdf

The device is not 1 cm, it is 'microns each side', right? That should make it roughly 10000x faster than 20 GHz, which is quite mind blowing for me. Especially if you imagine multi-layer superstructures for resolving very complex tasks with small amounts of energy.

The latency here is dominated by converting the 2D image to a 1D wavefront that's input to the device. This stage would involve some digital logic and relatively slow components with response time on the order of milliseconds.

This is the usual pipeline problem, though: sometimes the bottleneck is the CPU, and sometimes the bottleneck is memory bandwidth. This just places the ball firmly in the memory bandwidth court...

(You can have a hundred worker CPU cores doing the necessary conversions, but just need to worry about the parallelization complexity. But, then again, this is exactly what already happens when we feed data to hefty devices like GPUs and TPUs.)

Oh.

Makes me wonder if it could somehow act as logic gates using light.

Well you can implement a solver in hardware as well which will be very fast.