11–20 of 27 posts
I stopped reading after "Soon, you will not be able to afford your computer. Consumer GPUs are already prohibitively expensive."
It's like saying "cars are already prohibitively expensive" whilst looking a Ferraris.
I stopped reading after "Soon, you will not be able to afford your computer. Consumer GPUs are already prohibitively expensive."
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.
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
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.
- 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?
I stopped reading after "Soon, you will not be able to afford your computer. Consumer GPUs are already prohibitively expensive."
[1] https://www.msn.com/en-in/money/news/china-s-first-gaming-gp...
> 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?
I think they are just saying the coprocessor is pretty accurate, so they don’t need to use these advanced techniques.
Earlier quoted context omitted.
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.
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
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 do…
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 do…
For years I've been fascinated by those little solar-powered calculators. In a weird way, they're devices that enable us to cast hand shadows to do arithmetic.
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 I remember a TV Program in the UK from the 70's (tomorrows world I think) that talked about this s…