technology

The problem with digital computing is just a light switch that can be on or off. To model a smooth curve, like those sigmoid functions all over AI, it has to model it with a massive series of discrete steps. It's clunky and burns a ton of energy just to approximate a smooth line. But an analog computer can be any value in between. It doesn't have to calculate the gradient because its voltage just is the gradient. It's modeling the physics of the problem directly, which is why it's so much more efficient for this stuff.

Meanwhile, the five orders of magnitude precision claim is actually the most important part. The whole reason we ditched analog in the 70s was because it was noisy and imprecise. Their breakthrough is basically that they've made an analog system to have enough precision to make it practical.

And you're absolutely right that digital can do in-memory computing, but the way it's done is the key. Digital in-memory compute is still just moving 1s and 0s around using logic gates, just on the same chip. Meanwhile, analog RRAM stuff is using the physical properties of the resistive material to do the math. They feed a bunch of input voltages into a grid of these memory cells, and the output current that comes out is the answer to a massive matrix multiplication, all in one shot. The computation is a physical event, not a sequence of steps.

As for those 1,000x performance gains, that's not what you'll likely see in real world applications. This isn't going to run your OS or a web browser faster. It's a specialized accelerator, just like a GPU is for graphics. You'd still have a normal CPU, but it would hand off specific tasks to this thing.