This is a very specific technology that has serious scaling issues, and the neural networks coming out today are huge in comparison to YOLOv5 and ResNet. The company has already failed once. This will probably have its niche in some computer vision stuff for a little while, but models have already outgrown it.
This particular implementation of the idea of analog matrix processor implemented at low level may fail, but some different implementation will succeed.
There are some fundamental limits to analog computing that start to really hurt at the small nodes, which you need to scale up. There's a very good reason they are stuck at 40 nm.
Are you talking bjt or cmos now?
Theyre not stuck at 40nm due to technical constraints.
If they were using memristors sure, but theyre not really available yet.
I don't think 5nm CMOS is the winner if you compare the equivalent computational units in analog 40nm versus traditional CMOS memory+logic,
I have an analog circuits and a bit of an analog computing background and I will tell you this: analog computers have been around for awhile in commercial applications, still to this day theyre used to great success in some niche applications. They're limited by their flexibility as they are not really reprogrammable. Analog circuits need to be designed for each specific application. There are papers and prototypes built to create "reconfigurable" analog versions of FPGAs but they are limited by physical scalability issues, noise, routing, etc.
I agree with you completely on this, and I also have a bit of an analog circuits background. These chips are unfortunately probably better thought of as purpose-built accelerators for specific neural networks trained almost a decade ago than as the next paradigm of computing.
What got me into analog computing was eliminating the step (data conversion) between sensing the signal and doing something with it. But when you add in the inflexibility of analog circuits, the mostly analog solution is almost always the bad one.