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

Rice, Intel optimize AI training for commodity hardware
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 primarily based 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 Faculty of Engineering. “Firms are spending hundreds of 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 programs convention MLSys.

Deep neural networks (DNN) are a robust type of synthetic intelligence that may outperform people at some duties. DNN coaching is often a sequence of matrix multiplication operations, an excellent workload for graphics processing models (GPUs), which value about thrice greater than normal objective central processing models (CPUs).

“The entire business is fixated on one sort of enchancment—sooner matrix multiplications,” Shrivastava stated. “Everyone seems to be specialised {hardware} and architectures to push matrix multiplication. Individuals at the moment are even speaking about having specialised hardware-software stacks for particular sorts of deep studying. As a substitute 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 drawback that may very well 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 may outperform GPU-based coaching after they unveiled it at MLSys 2020.

The research they’re going to current this week at MLSys 2021 explored whether or not SLIDE’s efficiency may very well be improved with vectorization and reminiscence optimization accelerators in trendy CPUs.

“Hash table-based acceleration already outperforms GPU, however CPUs are additionally evolving,” stated research co-author Shabnam Daghaghi, a Rice graduate scholar. “We leveraged these improvements to take SLIDE even additional, displaying that for those who aren’t fixated on matrix multiplications, you’ll be able to leverage the facility in trendy CPUs and prepare AI fashions 4 to fifteen instances sooner than the perfect specialised {hardware} various.”

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

Deep studying rethink overcomes main impediment in AI business

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CPU algorithm trains deep neural nets as much as 15 instances sooner than high GPU trainers (2021, April 7)
retrieved 8 April 2021

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