The goal is not to multiply two analog voltages, but rather generic "matrix math". To quote original article: > Deep learning and recent transformer models are

The goal is not to multiply two analog voltages, but rather generic "matrix math". To quote original article: > Deep learning and recent transformer models are really just matrix multiplication machines. Analog integrated circuits are well-suited for efficiently implementing matrix multiplication operations. [...] Keeping computations in the analog domain avoids costly digitization and leverages the parallelism and density possible on an IC substrate. I call BS on this. You are never going to have enough precision in analog world to do what top current ML models do. And even if you can create model dumbed down enough to run on analog, it's cost (and power usage) is going to be astronomical.