This is a fun idea. What surprised me is the inversion where MUL ends up faster than ADD because the neural LUT removes sequential dependency while the adder still needs prefix stages.
Ya know just today I was thinking around a way to compile a neural network down to assembly. Matching and replacing neural network structures with their closest machine code equivalent.
This is way cooler though! Instead of efficiently running a neural network on a CPU, I can inefficiently run my CPU on neural network! With the work being done to make more powerful GPUs and ASICs I bet in a few years I'll be able to run a 486 at 100MHz(!!) with power consumption just under a megawatt! The mind boggles at the sort of computations this will unlock!
Few more years and I'll even be able to realise the dream of self-hosting ChatGPT on my own neural network simulated CPU!
A fun experiment but I wonder how many out there seriously think we could ever completely rid ourselves of the CPU. It seems to be a rising sentiment.
The cost of communicating information through space is dealt with in fundamentally different ways here. On the CPU it is addressed directly. The actual latency is minimized as much as possible, usually by predicting the future in various ways and keeping the spatial extent of each device (core complex) as small as possible. The GPU hides latency with massive parallelism. That's why we can put them across relatively slow networks and still see excellent performance.
Latency hiding cannot deal well in workloads that are branchy and serialized because you can only have one logical thread throughout. The CPU dominates this area because it doesn't cheat. It directly targets the objective. Making efficient, accurate control flow decisions tends to be more valuable than being able to process data in large volumes. It just happens that there are a few exceptions to this rule that are incredibly popular.
Every clueless person who suggest that we move to GPUs entirely have zero idea how things work and basically are suggesting using lambos to plow fields and tractors to race in nascar
Get used to it. The modern day solution for everything right now is to throw AI at it.
Hmmm... I need to measure this piece of wood for cutting, let me take a picture of it and see what the ai says its measurement is instead of using a measuring tape because it is faster to use the AI.
Exciting if an Ai that is helping in its own improvements finds this and incorporates it into its own architecture. Then it starts reading and running all the worlds binary and gains intelligence as a fully actualized "computer". Finally becoming both a master of language and of binary bits. Thinking in poetry and in pure precise numerical calculations.
How is this different than the (various?) efforts back then to build a machine based on the Intel i860? Didn’t work, although people gave it a good try.
Hey everyone thank you taking a look at my project. This was purely just a “can I do it” type deal, but ultimately my goal is to make a running OS purely on GPU, or one composed of learned systems.
Depends entirely on your definition of 'entirely', but https://github.com/jhuber6/doomgeneric is pretty much a direct compilation of the DOOM C source for GPU compute. The CPU is necessary to read keyboard input and present frame data to the screen, but all the logic runs on the GPU.
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[ 3.2 ms ] story [ 68.4 ms ] threadWow. That's cool but what happens to the regular CPU?
[1]: https://breandan.net/2020/06/30/graph-computation#roadmap
This is way cooler though! Instead of efficiently running a neural network on a CPU, I can inefficiently run my CPU on neural network! With the work being done to make more powerful GPUs and ASICs I bet in a few years I'll be able to run a 486 at 100MHz(!!) with power consumption just under a megawatt! The mind boggles at the sort of computations this will unlock!
Few more years and I'll even be able to realise the dream of self-hosting ChatGPT on my own neural network simulated CPU!
Most GPUs, sitting in racks in datacenters, aren't "processing graphics" anyhow.
Also, is it possible to use the GPU's ADD/MUL implementation? It is what a GPU does best.
The cost of communicating information through space is dealt with in fundamentally different ways here. On the CPU it is addressed directly. The actual latency is minimized as much as possible, usually by predicting the future in various ways and keeping the spatial extent of each device (core complex) as small as possible. The GPU hides latency with massive parallelism. That's why we can put them across relatively slow networks and still see excellent performance.
Latency hiding cannot deal well in workloads that are branchy and serialized because you can only have one logical thread throughout. The CPU dominates this area because it doesn't cheat. It directly targets the objective. Making efficient, accurate control flow decisions tends to be more valuable than being able to process data in large volumes. It just happens that there are a few exceptions to this rule that are incredibly popular.
I imagine a carefully crafted set of programming primitives used to build up the abstraction of a CPU…
“Every ALU operation is a trained neural network.”
Oh… oh. Fun. Just not the type of “interesting” I was hoping for.
Hmmm... I need to measure this piece of wood for cutting, let me take a picture of it and see what the ai says its measurement is instead of using a measuring tape because it is faster to use the AI.
Thank you, Mr. Do-because-I-can!
Yours truly,
- GPU company CEO,
- Electric company CEO.
Can't wait for someone to build a DOOM that runs entirely on GPU!