[tdrdt]

But arent there other applications where this is ok?

For example path tracing (ray tracing) doesn't need to be very accurate because multiple samples per pixel are used.

A gaming card that uses less power is very welcome in laptops for example.

[moonchrome]

What do you mean? What kind of worlds can you represent with 8 bits units ? Some small blocky voxel box ?

[tdrdt]

I mean inacurate vector math

[moonchrome]

But that is what I'm saying if your vectors are reduced to 8 bit scalar components you can represent a 256x256x256 worth of detail in the world (doesn't need to be linear but still really limited details) ?

[montjoy]

I’m totally out of my expertise here but I have a question - from my understanding ray tracing is primarily used for lighting/shadows/reflections- wouldn’t it be OK for something like shadows to be inaccurate- maybe some sort of amalgamation over frame refreshes? Real world light is messy anyway. I’m talking about a game type scenario not something scientific.

Maybe another way to ask is- we’re trying to simulate a real world “analog” scene - maybe using an analog processing technique could actually be quite faithful for generating it?

Or not. Like I said I don’t understand too much of this.

[moonchrome]

Think about it like this : you have a spaceship model fits in 256x256x256m - to get the maximum resolution while still fitting in 8 bits you would make each axis in 1m increments and you have 256 values. So you can't have sub 1m details in geometry. Floating point is different because you have an exponent so it's not linear, you can technically have larger scale, but you sacrifice even more precision in mantissa.

Now I'm not sure what this 6/8 bit precision or analog precision means so I can't say with confidence, but if your scalars are that low precision you can't really do much. You could technically encode it with some fancy tricks like instead of storing coordinates for each vertex you store the delta from previous one etc. but I think this wouldn't work if the device was just some dumb analog matrix multiplier with baked logic.

Also having low detail shadows creates visual artifacts, see this for example [1]

[1] https://digitalrune.github.io/DigitalRune-Documentation/medi...

[montjoy]

Thank you for the reply. The article didn’t give details what it how it actually works so I went directly to the company’s site and found this [1]

> … leverages light scattering to perform a specific kind of matrix-vector operation called Random Projections. … have a long history for the analysis of large-size data since they achieve universal data compression. In other words, you can use this tool to reduce the size of any type of data, while keeping all the important information that is needed for Machine Learning.

So it sounds like the whole point is to reduce the data size and then feed it into GPUs like normal ML. Kind of a neat idea.

[1] https://lighton.ai/photonic-computing-for-ai/