collider

metastability #webgpu

primordial partical system with high partical speeds and huge interaction radius.

when beta is small negative number, particles tend to cluster together. If beta is a bigger negative number, behaviour becomes chaotic.
As always, there is an interesting region in the parameter space between those two phases

https://compute.toys/view/359

[WIP] refactor NCA to use array structs, leveraging webgpu capabilities #webgpu

simple implementation of texture NCA in webgpu compute shaders
https://compute.toys/view/359
(code for train & export your own model is in the next post)

https://github.com/algroznykh/notebooks/blob/master/%CE%BCNCA_pytorch_compute_codegen.ipynb
code for training own model & export to compute.toys

While refactoring NCA to use webgpu arrays, I've encountered a strange performance problem:
in 1x1 conv stage, when there is 12*48 cycles to compute state update, my machine initially hanged after ~50 cycles.
It turned out, that accessing weights that were defined as top-level const is very inefficient for some reason.
copying weight matrix to a local variable in the function body solved it 🤷

new neural cellular automata demo + recap of recent papers
https://google-research.github.io/self-organising-systems/isonca/

comet

colab introduced "compute units" which are issued once a month on a pro subnoscription, but with my current throughput, burned in a day. This is an equivalent of a 30-fold price increase. Time to look for a self-hosted training solution.
(TBH I can train NCA even on my laptop, which performs better than colab's free tier on all specs, except vmem, but then I have difficulties to run shaders on the same time)

https://arxiv.org/pdf/2302.10197.pdf
I've had the exact same idea about controlling rotation of the sobel kernels via a parameter in a hidden states! Nice to see it validated
(btw did anyone already tried to train a vision model with filters defined as an sdf?)

defined sobel filters in perception phase as sdf, now I can compute convolutions of any size.
Although after 11x11 performance starts to drop a little (I have 8 of them in each agent)