SFI had one of these for a while.  (As far as I know it just sat there.)

http://www.ai.mit.edu/projects/im/cam8/

Nowadays GPUs are used for Lattice Boltzmann.


such a blast-from-past with the awesome 90's stylized web page and the pics of the SUN (and Apollo?) workstations!

CAM8 is clearly the legacy of Margolis' work (MIT).   At the time (1983) I remember handwired/soldered breadboards and I think banks of memory chips wired through logic gates and such... I think this was pre SUN days (I had an M68000 Wicat Unix box on my desktop which sported a massive 5MB hard drive with pruned down BSD variant installed on it).  In fact, that was where I ran the MT simulations (tuning rules until I got "interesting" activity, running parameter sweeps, etc).

When GPUs first rose up (SGI) they seemed hyper-apropriate to the purpose but alas, I had not spare cycles at that point in my career to look into it.  Just a few years ago when I was working on the Micoy Omnistereoscopic "camera ball" (you mentioned it looked a bit like  coronavirus particle) I had specced out an FPGA fabric solution (with a dedicated FPGA wired directly between every adjacent overlapping camera pair - 52 cameras) to do realtime image de-distortion/stitching with the special considerations which stereo vison adds.   I never became a VHDL programmer but I did become familiar with the paradigm... I think I tried to engage Roger at a Wedtech on the topic when he was (also) investigating FPGAs.  (circa 2016?)

At that time, my fascination with CA had evolved into variations on Gosper's Hashlife...  so GPU and FPGA fabric didn't seem as apt, though TPUs do seem (more) apt for the implicit data structures (hashed quad-trees).


The new nVidia DGX concentrated TPU system for $3k is fascinating and triggers my thoughts (not very coherent) about the tradeoffs between power and entropy and "complexity".

A dive down this 1983/4 rabbit hole lead me (also) to the /Toffoli/ and /Fredkin Gates/ and /Reversible Computing. /More on that in a few billion more neural/GPT cycles...

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