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.
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".
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.
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.