Fujitsu Memory Tech Speeds Up Deep-Learning AI

Artificial intelligence driven by deep learning often runs on many computer chips working together in parallel. But the deep-learning algorithms, called neural networks, can run only so fast in this parallel computing setup because of the limited speed with which data flows between the different chips. The Japan-based multinational Fujitsu has come up with a novel solution that sidesteps this limitation by enabling larger neural networks to exist on a single chip. The neural networks used in deep learning typically run on graphics processing units (GPUs) that originated as components for generating and displaying images. By creating an efficiency shortcut in the calculations performed by neural networks, Fujitsu researchers reduced the amount of internal GPU memory used by 40 percent.