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Comment by coder543 | Hacker News Reader

> It's odd to me that I haven't heard anything about this approach since.

It has only been four months since they unveiled their first prototype. I don't understand your confusion. Chip development does not happen overnight...?

Their initial blog post laid out a roadmap, so theoretically they should have another thing to demonstrate this summer.

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In the sense of interested customers, online discussion, other companies doing the same thing, etc. Of course it takes time to get actual results, but from an outsider's perspective it's surprising that it was basically just their initial demo and that's more or less it so far. Excited to see if they come out with something this summer though!

You are focusing on Taalas, but (specific) analogue computing, electronic NNs, compute-in-memory etc. - the field including the contextual approach - backdate to Rosenblatt.

Yes, I’m focused on the topic at hand that the person I replied to was also talking about.

The person I replied to was acting as if Taalas was ancient history. I was pointing out it has only been a few months.

I'd say the original remark was more general («this approach (baking LLMs/weights into silicon directly) [... as if] worked on in secret») - which is salient, because when I investigated weeks ago, I found a large number of attempts to CIM and to general branching from Von Neumann architecture for the purpose of optimizing NNs implementations in HW.

Universities are studying, startups are proposing - the «approach» is under the big headlines level but quite lively. Not just Taalas, not just their way - which remains remarkable in the scene as the HW is achieved, working, online, available... and amazing.

CIM does not bake the weights into silicon. The level of optimization that you can do down to the last transistor when the weights are fixed is on an entirely different level than CIM where you still need general purpose ALUs all over the place.

> CIM does not bake the weights into silicon

If that were the extent of the terms, then what could we call "baking the weights into silicon"? Setting parts of the circuits to determined values for multiplication is is like printing a Read-Only Memory. (And you compute at it: Compute In Memory.)

> CIM where you still need general purpose ALUs all over the place

If that were so, then why do taxonomists present analogue computing as part of CIM? Ohm's Law does not constitute an "ALU" the way you intend it.

Simply, I used CIM, "Compute In Memory", for lack of a better term - for "store data there where you modify data", for "beyond Von Neumann's separation of data storage and processor".

EDIT: It's just not even worth arguing this point, so deleting my original, much longer comment. Abstract taxonomies can claim that Taalas is CIM, but this entirely and utterly misses the point, and misses what makes Taalas' approach special. If you told a room full of chip architects to go build "CIM for AI", they would not build a Taalas-like totally specialized chip, therefore it is not sufficient, and just muddies the conversation from my point of view. People have been doing "CIM" for decades and yet I've never seen anyone build a totally specialized chip at the scale of Taalas. And yes, you can (in theory) build an analog version of any computer, so of course you can build analog CIM, but "analog compute" is not inherently CIM, so conflating the two is just confusing.

I can't check everything right now, but for example, the divulgational from Rakesh Kumar mentiones "Analogue CIM".

And I do not get your rant about "analog computing", which has everything to do with NNs (otherwise, well, prove it): they started with that - they are basically that in fact. Analogue computing is a very great temptation since it would solve the issues of inefficiency in digital NNs. Unfortunately, it has drawbacks which are massive for big NNs. Taalas' seems to be the best compromise.

(Note just for homekeeping: this replied to the original parent response, later edited. The parallel ("Pretty simply") replied to the edited response.)

> It's just not even worth arguing this point

Pretty simply: the von Neumann architecture separates data storage and processing; we are now researching the alternatives - in all their forms, whatever allows to make NNs efficient; those alternatives basically focus on reducing the distance between stored data and its processing; some simply call stored data "memory"; we had the concept of "Compute in Memory" available; now some are calling "CIM" all of the relevant solutions that allow processing in the vicinity of the storage.

It is not the traditional CIM with the ALUs. Too bad. We can easily get the difference.

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