Machine learning at speed
Inserting lightweight optimization code in high-speed network devices has enabled a KAUST-led collaboration to increase the speed of machine learning on parallelized computing systems five-fold.
This "in-network aggregation" technology, developed with researchers and systems architects at Intel, Microsoft and the University of Washington, can provide dramatic speed improvements using readily available programmable network hardware.
The fundamental benefit of artificial intelligence (AI) that gives it so much power to "understand" and interact with the world is the machine-learning step, in which ...










