>For current silicon-based technologies to categorize data from large sets like ECGs, it takes more than 100 transistors — each requiring its own energy to run.

>For current silicon-based technologies to categorize data from large sets like ECGs, it takes more than 100 transistors — each requiring its own energy to run. But Northwestern’s nanoelectronic device can perform the same machine-learning classification with just two devices. Who is going to break it to them that we've long, long ago passed the era of 7 transistor radios? Most PCs have billions of transistors in them. If they're just going to run a neural network, you could emulate it on an RP2040 with plenty of computational headroom, that's a $1 CPU.