Those are amazing accomplishments but I am more interested in research developments in things like In-Memory (Analog) or other different approaches.
Companies like EnCharge, Mythic, etc.
And much more efficient devices like RRAM, MRAM, and FETs. Like FE-FETs with AlScN.
The stuff just coming out of research or still in research is more exciting in terms of the potential for truly huge efficiency and performance boosts.
Taalas is interesting also because of it's efficiency and speed. Guess it was just purchased by AMD.
It’s shorthand for “it’s unreadable, but in the way an AI would write it”. I’m not OP, but that’s the issue. If it was well written (even if AI written), there would be no problem.
As it turns out, you still need to be a good writer in order to prompt an AI to write well.
def. AI-assisted, both content and style style/nav.
Content-wise, it looks legit, but with that amount of stuff, it's nearly impossible to say for sure.
Maybe it's just me, but between the extremely thin font and layout design, I find this extremely difficult to parse. Overuse and improper use of italics is confusing as well.
I have a feeling the hardware architecture for LLMs are completely wrong. There's no way hundreds of kilowatts is required for intelligence.. just in terms of the physics. Is there someone out there in the analog/neuromorphic computing world that could make these power-hungry monsters completely redundant?
It's more the economics of what sells that lets the power hungry monsters thrive. If you can tape out one chip design, and one gives you 1 TOPS and the other is 5 TOPS, the sales will go to the 5 TOPS because power is a downstream cost. That being said, it's only a matter of time before both chips are maximum TOPS and the only differentiator is power. My latest simulations are showing a competitive design around 70W, but I'm still confirming it and it would really be beneficial to do some data gathering tapeouts at larger nodes to feel like it's worth the risk of more expensive nodes.
Companies like EnCharge, Mythic, etc.
And much more efficient devices like RRAM, MRAM, and FETs. Like FE-FETs with AlScN.
The stuff just coming out of research or still in research is more exciting in terms of the potential for truly huge efficiency and performance boosts.
Taalas is interesting also because of it's efficiency and speed. Guess it was just purchased by AMD.
When the density of italicized words is about 1:1, I'm out.
As it turns out, you still need to be a good writer in order to prompt an AI to write well.
Ignorant people are always certain.
Not slope tho!