CPU algorithm trains deep neural nets as much as 15 instances sooner than prime GPU trainers

Anshumali Shrivastava is an assistant professor of laptop science at Rice College. Credit score: Jeff Fitlow/Rice College

Rice College laptop scientists have demonstrated synthetic intelligence (AI) software program that runs on commodity processors and trains deep neural networks 15 instances sooner than platforms based mostly on graphics processors.

“The price of coaching is the precise bottleneck in AI,” stated Anshumali Shrivastava, an assistant professor of laptop science at Rice’s Brown College of Engineering. “Firms are spending thousands and thousands of {dollars} per week simply to coach and fine-tune their AI workloads.”

Shrivastava and collaborators from Rice and Intel will current analysis that addresses that bottleneck April 8 on the machine studying techniques convention MLSys.

Deep neural networks (DNN) are a strong type of synthetic intelligence that may outperform people at some duties. DNN coaching is often a collection of matrix multiplication operations, a really perfect workload for graphics processing items (GPUs), which value about 3 times greater than common objective central processing items (CPUs).

“The entire business is fixated on one form of enchancment—sooner matrix multiplications,” Shrivastava stated. “Everyone seems to be specialised {hardware} and architectures to push matrix multiplication. Folks are actually even speaking about having specialised hardware-software stacks for particular sorts of deep studying. As an alternative of taking an costly algorithm and throwing the entire world of system optimization at it, I am saying, ‘Let’s revisit the algorithm.'”

Shrivastava’s lab did that in 2019, recasting DNN coaching as a search downside that might be solved with hash tables. Their “sub-linear deep studying engine” (SLIDE) is particularly designed to run on commodity CPUs, and Shrivastava and collaborators from Intel confirmed it might outperform GPU-based coaching once they unveiled it at MLSys 2020.

The research they will current this week at MLSys 2021 explored whether or not SLIDE’s efficiency might be improved with vectorization and reminiscence optimization accelerators in fashionable CPUs.

“Hash table-based acceleration already outperforms GPU, however CPUs are additionally evolving,” stated research co-author Shabnam Daghaghi, a Rice graduate pupil. “We leveraged these improvements to take SLIDE even additional, exhibiting that in case you aren’t fixated on matrix multiplications, you possibly can leverage the facility in fashionable CPUs and prepare AI fashions 4 to fifteen instances sooner than the most effective specialised {hardware} different.”

Examine co-author Nicholas Meisburger, a Rice undergraduate, stated “CPUs are nonetheless essentially the most prevalent {hardware} in computing. The advantages of creating them extra interesting for AI workloads can’t be understated.”


Deep studying rethink overcomes main impediment in AI business


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