[tomohelix]

The relevant paper: https://arxiv.org/abs/2406.02528

In summary, they forced the model to process data in ternary system and then build a custom FPGA chip to process the data more efficiently. Tested to be "comparable" to small models (3B), theoretically scale to 70B, unknown for SOTAs (>100B params).

We have always known custom chips are more efficient especially for tasks like these where it is basically approximating an analog process (i.e. the brain). What is impressive is how fast it is prgressing. These 3B params models would demolish GPT2 which was, what, 4-5 years old? And they would be pure scifi tech 10 years ago.

Now they can run on your phone.

A machine, running locally on your phone, that can listen and respond to anything a human may say. Who could have confidently claim this 10 years ago?

[roenxi]

I was confused by the claim in the headline but it seems that this is really the meat of the paper - they're looking for an architecture that is more efficient to implement and run in hardware. It is interesting. We know that computers must be wasting huge amounts of compute on something by analogy with a human brain and researchers will figure out why sooner or later.

[bee_rider]

With a ternary system, would we expect 1/3 of the elements to be zero? I kind of wonder about using a sparse MM, then they wouldn’t have to represent 0 and the could just use one bit to represent 1 or -1. 66% density is not really very sparse at all though.