areddyyt

Our CPU implementation for X86/AMD64 utilizes AVX-512 or AVX-2 instructions where possible. We're experimenting with support for ARM with NEON.

100%.

When performing performance optimization on CPUs, I was impressed with Intel's suite of tools (like VTUNE). NVIDIA has some unbelievable tools, like Nsys and, of course, its container registry (NGC), which I think surpasses even Intel's software support.

I should note that our linear layers are not the same as Microsoft's, in fact, we think Microsoft made a mistake in the code they uploaded. When I have time later today, I'll link to where I think they made a mistake.

I've been following TriLLM. They've achieved great results, and I'm really impressed with the llama.cpp contributors already getting the models integrated.

I don't think I ever implied we started this for money. We started working on the technology because it was exciting and enabled us to run LLMs locally. We wouldn't have started this company if someone else came along and did it, but we waited a month or two and didn't see anyone making progress. It just so happens that hardware is capital intensive, so making hardware means you need access to a lot of capital through grants (which Dartmouth didn't have for chip hardware) or venture capital (which we're going for now). I'm not sure where you got the idea we're doing this solely for money when I explicitly said "We were essentially nerd-sniped into working on this problem"

I think there is a comment somewhere here where I comment on NVIDIA, but I think NVIDIA is the best hardware company for making good software. We had a very niche software issue for which NVIDIA maintained open-source repos. I don't think NVIDIA's main advantage is its hardware, though; I think it's the software and the flexibility it brings to its hardware.

Suppose that Transformers die tomorrow, and Mamba becomes all the rage. The released Mamba code already has CUDA kernels for inference and training. Any of the CSPs or other NVIDIA GPU users can switch their entire software stack to train and inference Mamba models. Meanwhile, we'll be completely dead in the water with similar companies that made the same bet, like Etched.

We were waiting for a Bitnet-based software and hardware stack, particularly from Microsoft, but it never did. We were essentially nerd-sniped into working on this problem, then we realized it was also monetizable.

On a side note, I deeply looked into every company in the space and was thoroughly unimpressed with how little they cared about the software stack to make their hardware seamlessly work. So, even if I did go to work at some other hardware company, I doubt a lot of customers would utilize the hardware.

We do quantization-aware training, so the model should minimize the loss w.r.t. the ternary weights, hence no degradation in performance.

There was another founder that said this exact same thing. We'll definitely look into it especially as we train more ViTs.

Funnily enough, our ML engineer, Eddy, did a hackathon project working with Procyon to make a neural network with a photonic chip. Unfortunately, I think Lightmatter beat us to the punch.

Edit: I don't think the company exists in its current form anymore

Have you sat in on my conversations with my cofounder?

The end plan is to have a single chip and flush all weights onto the chip at initialization. Because we are a single line of code that is Torch compatible (hence HF compatible), every other part of the codebase shouldn't change.

This seems super cool. I'll have my cofounder look into it :)

We don't achieve peak compression efficiency because more complex weight unpacking mechanisms kill throughput.

To be more explicit, the weight matrix's values belong to the set of -1, 0, and 1. When using two bits to encode these weights, we are not effectively utilizing one possible state:

10 => 1, 01 => 0, 00 =>-1, 11 => ?

I think selecting the optimal radix economy will have more of a play on custom silicon, where we can implement silicon and instructions to rapidly decompress weights or work with the compressed weights directly.

Thank you, and good catch.

We recently acquired deepsilicon.com, and it looks like the forwarding hasn't been registered yet. [email protected] should work.

We actually were thinking about this to flush the weights in at initialization

It's always possible, but transformers have been around since 2017 and don't seem to be going anywhere. I was bullish on Mamba and researched extended context for structured state-space models at Dartmouth. However, no one cared. The bet we're taking is that Transformers will dominate for at least a few more years, but our bet could be wrong.