Startup’s Analog AI Promises Power for PCs

Naveen Verma’s lab at Princeton University is like a museum of all the ways engineers have tried to make AI ultra-efficient by using analog phenomena instead of digital computing. At one bench lies the most energy-efficient magnetic-memory-based neural-network computer ever made. At another you’ll find a resistive-memory-based chip that can compute the largest matrix of numbers of any analog AI system yet. Neither has a commercial future, according to Verma. Less charitably, this part of his lab is a graveyard. Analog AI has captured chip architects’ imagination for years. It combines two key concepts that should make machine learning massively less energy intensive. First, it limits the costly movement of bits between memory chips and processors. Second, instead of the 1s and 0s of logic, it uses the physics of the flow of current to efficiently do machine learning’s key computation. As attractive as the idea has been, various analog AI schemes have not delivered in a way that could r