Deep learning rethink overcomes major obstacle in AI industry: SLIDE is first algorithm for training deep neural nets faster on CPUs than GPUs

Computer scientists from Rice, supported by collaborators from Intel, will present their results today at the Austin Convention Center as a part of the machine learning systems conference MLSys. Many companies are investing heavily in GPUs and other specialized hardware to implement deep learning, a powerful form of artificial intelligence that's behind digital assistants like Alexa and Siri, facial recognition, product recommendation systems and other technologies. For example, Nvidia, the maker of the industry's gold-standard Tesla V100 Tensor Core GPUs, recently reported a 41% increase in its fourth quarter revenues compared with the previous year. Rice researchers created a cost-saving alternative to GPU, an algorithm called "sub-linear deep learning engine" (SLIDE) that uses general purpose central processing units (CPUs) without specialized acceleration hardware. "Our tests show that SLIDE is the first smart algorithmic implementation of deep learning on CPU that can outperform GPU hardware acceleration on industry-scale recommendation datasets with large fully connected architectures," said Anshumali Shrivastava, an assistant professor in Rice's Brown School of Engineering who invented SLIDE with graduate students Beidi Chen and Tharun Medini.