Training neural networks to perform tasks, such as
recognizing images or navigating self-driving cars, could one day
require less computing power and hardware thanks to a new
artificial neuron device developed by researchers at the University
of California San Diego. The device can run neural network
computations using 100 to 1000 times less energy and area than
existing CMOS-based hardware.
Artificial neuron device could shrink energy use and size of
neural network hardware
