[HN Gopher] A New Kind of Computer (April 2025)
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A New Kind of Computer (April 2025)
Author : gkolli
Score : 27 points
Date : 2025-06-24 03:27 UTC (3 days ago)
(HTM) web link (lightmatter.co)
(TXT) w3m dump (lightmatter.co)
| croemer wrote:
| I stopped reading after "Soon, you will not be able to afford
| your computer. Consumer GPUs are already prohibitively
| expensive."
| kevin_thibedeau wrote:
| This is always a hilarious take. If you inflation adjust a 386
| PC from the early 90s when 486's were on the market you'd find
| they range in excess of $3000 and the 486s are in the $5000
| zone. Computers are incredibly cheap now. What isn't cheap is
| the bleeding edge. A place fewer and fewer people have to be
| at, which leads to lower demand and higher prices to
| compensate.
| ge96 wrote:
| It is crazy you can buy a used laptop for $15 and do
| something meaningful with like writing code (meaningful as in
| make money)
|
| I used to have this weird obsession of doing this, buying old
| chromebooks putting linux on them, with 4GB of RAM it was
| still useful but I realize nowadays for "ideal" computing it
| seems 16GB is a min for RAM
| ge96 wrote:
| It's like the black Mac from 2007, I know its tech is
| outdated but I want it
| TedDallas wrote:
| It was kind or that way in early days of high end personal
| computing. I remember seeing an ad in the early 90s for a 486
| laptop that was $6,000. Historically prices have always gone
| down. You just have to wait. SoTA is always going to go for a
| premium.
| ghusto wrote:
| That irked me too. "_Bleeding edge" consumer GPUs are ...",
| sure, but you wait 6 months and you have it at a fraction of
| the cost.
|
| It's like saying "cars are already prohibitively expensive"
| whilst looking a Ferraris.
| Animats wrote:
| That's related more to NVidia's discovery that they could get
| away with huge margins, and the China GPU projects for graphics
| being years behind.[1]
|
| [1] https://www.msn.com/en-in/money/news/china-s-first-gaming-
| gp...
| Anduia wrote:
| > Critically, this processor achieves accuracies approaching
| those of conventional 32-bit floating-point digital systems "out-
| of-the-box," without relying on advanced methods such as fine-
| tuning or quantization-aware training.
|
| Hmm... what? So it is not accurate?
| btilly wrote:
| It's an analog system. Which means that accuracy is naturally
| limited.
|
| However a single analog math operation requires the same energy
| as a single bit flip in a digital computer. And it takes a lot
| of bit flips to do a single floating point operation. So a
| digital calculation can be approximated with far less energy
| and hardware. And neural nets don't need digital precision to
| produce useful results.
| B1FF_PSUVM wrote:
| > neural nets don't need digital precision to produce useful
| results.
|
| The point - as shown by the original implementation...
| bee_rider wrote:
| It seems weirdly backwards. They don't do techniques like
| quantization aware tuning to increase the accuracy of the
| coprocessor, right? (I mean that's nonsense). They use those
| techniques, to allow them to use less accurate coprocessors, I
| thought.
|
| I think they are just saying the coprocessor is pretty
| accurate, so they don't need to use these advanced techniques.
| btilly wrote:
| This paradigm for computing was already covered three years ago
| by Veratasium in https://www.youtube.com/watch?v=GVsUOuSjvcg.
|
| Maybe not the specific photonic system that they are describing.
| Which I'm sure has some significant improvements over what
| existed then. But the idea of using analog approximations of
| existing neural net AI models, to allow us to run AI models far
| more cheaply, with far less energy.
|
| Whether or not this system is the one that wins out, I'm very
| sure that AI run on an analog system will have a very important
| role to play in the future. It will allow technologies like
| guiding autonomous robots with AI models running on hardware
| inside of the robot.
| boznz wrote:
| Weirdly complex to read yet light on key technical details. My
| TLDR (as an old clueless electronics engineer) was the compute
| part is photonic/analog, lasers and waveguides, yet we still
| require 50 billion transistors performing the (I guess non-
| compute) parts such as ADC, I/O, memory etc. The bottom line is
| 65 TOPS for <80W - The processing (optical) part consuming 1.65W
| and the 'helper electronics' consuming the rest so scaling the
| (optical) processing should not have the thermal bottlenecks of a
| solely transistor based processor. Also parallelism of the
| optical part though using different wavelengths of light as
| threads may be possible. Nothing about problems, costs, or can
| the helper electronics eventually use photonics.
|
| I remember a TV Program in the UK from the 70's (tomorrows world
| I think) that talked about this so I am guessing silicon was just
| more cost effective until now. Still taking it at face value I
| would say it is quite an exciting technology.
| quantadev wrote:
| In 25 years we'll have #GlassModels. A "chip", which is a passive
| device (just a complex lens) made only of glass or graphene,
| which can do an "AI Inference" simply by shining the "input
| tokens" thru it. (i.e. arrays of photons). In other words, the
| "numeric value" at one MLP "neuron input" will be the amplitude
| of the light (number of simultaneous photons).
|
| All addition, multiplication, and tanh functions will be done by
| photon superposition/interference effects, and it will consume
| zero power (since it's only a complex "lens").
|
| It will probably do parallel computations where each photon
| frequency range will not interfere with other ranges, allowing
| multiple "inferences" to be "Shining Thru" simultaneously.
|
| This design will completely solve the energy crisis and each
| inference will take the same time as it takes light to travel a
| centimeter. i.e. essentially instantaneous.
| Animats wrote:
| Interesting. Questions, the Nature paper being expensively
| paywalled:
|
| - Is the analog computation actually done with light? What's the
| actual compute element like? Do they have an analog photonic
| multiplier? Those exist, and have been scaling up for a while.[1]
| The announcement isn't clear on how much compute is photonic.
| There are still a lot of digital components involved. Is it worth
| it to go D/A, generate light, do some photonic operations, go
| A/D, and put the bits back into memory? That's been the classic
| problem with photonic computing. Memory is really hard, and
| without memory, pretty soon you have to go back to a domain where
| you can store results. Pure photonic systems do exist, such as
| fiber optic cable amplifiers, but they are memoryless.
|
| - If all this works, is loss of repeatability going to be a
| problem?
|
| [1] https://ieeexplore.ieee.org/document/10484797
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