Re Reversible Computing and Fredkin/Toffoli gates. I'm fascinated with
the apparent lack of progress in this general field.
When they spoke on this topic in 1983 (and I think Feynman referenced it
in his updated "plenty of room at the bottom" (ca 1959) talk in the
context of speculating as to whether "molecular computing" might be a
good candidate for this style of reversibility. Every time it comes
up (every decade?) I kick myself for not paying closer attention, but
the lack of progress suggests it is "before it's time" or perhaps a
total "wild goose".
I don't remember if they referenced "adiabatic" computing and I have not
followed up references to understand that trade-space... it feels like a
TANSTAAFL argument which only carries traction in edge/corner cases,
though computing in biologic and (other) molecular scale contexts might
well make that trade (to avoid thermal problems)? Whether Universal
Assembler NT or biologic self-assembly "circuits".
What *little* update I've been able to obtain specifically on Toffoli
and Fredkin gate based reversible circuits suggests that the space/time
costs is order 4X to 8X in combined increased real-estate and latency?
Intuition suggests to me that such is worth it in these new giga-scale
AI training contexts, if the thermal gains are as significant as
suggested. Theoretically the reversibility and thermodynamic
implications might be absolute but practically not (at least in
electronic circuits, maybe not in photonic?).
The most recent survey paper I found was 2013 and it already seems too
dense for me, so maybe I will stall in this quest/reflection.
https://arxiv.org/pdf/1309.1264
I understand that Quantum Computing (in some forms?) is reversible so
some/many of the issues are likely shared? Our resident Quantum
Alchemist (or other CS/EECE wizards here) might be able to shed some light?
- Steve
On 1/11/25 8:38 AM, steve smith wrote:
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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