I’ve been following IBM’s AI efforts with interest for a quite a while now. In my opinion, the company jump-started the current cycle of AI with the introduction of Watson back in the 2000s and has steadily been ramping up its efforts since then. Most recently, I wrote about the launch of PowerAI, IBM’s software toolkit solution to use with OpenPOWER systems for enterprises who don’t want to develop their AI solutions entirely from scratch but still want to be able to customize to fit their specific deep learning needs. Today, IBM Research announced a new breakthrough that will only serve to further enhance PowerAI and its other AI offerings—a groundbreaking Distributed Deep Learning (DDL) software, which is one of the biggest announcements I’ve tracked in this space for the past six months.
Getting rid of the single-node bottleneck
Anyone who has been paying attention knows that deep learning has really taken off in the last several years. It’s powering hundreds of applications, in consumer as well as business realms, and continues to grow. One of the biggest problems holding back the further proliferation of deep learning, however, is the issue of scalability. Most AI servers today are just one single system, not multiple systems combined. The most popular open-source deep learning software frameworks simply don’t perform well across multiple servers, creating a time-consuming bottleneck. In other words, while many data scientists have access to servers with four to eight GPUs, they can’t take advantage of it and scale beyond the single node—at the end of the day, the software just wasn’t designed for it.
Enter the IBM DDL library: a library built with IBM Research’s unique clustering methods, that links into leading open-source AI frameworks (such as TensorFlow, Caffee, Torch, and Chainer). With DDL, these frameworks can be scaled to tens of IBM servers, taking advantage of hundreds of GPUs—a night and day difference from the old model of doing things. To paint a picture, when IBM initially tried to train a model with the ImageNet-22K data set, using a ResNet-101 model, it took 16 days on a single Power “Minsky” server, using four NVIDIA P100 GPU accelerators. A 16-day training run means a significant delay of time to insight, and can seriously hinder productivity.
IBM is calling DDL “the jet engine of deep learning”—a catchy moniker that honestly isn’t too far off the mark in my opinion. Using DDL techniques, IBM says it was able to cut down that same process to a mere 7 hours, on 64 Power “Minsky” servers, with a total of 256 NVIDIA P100 GPU accelerators. Let me reiterate that: 16 days, down to 7 hours. If these results are accurate, which I think they are, it’s clear why IBM thinks it has a real game-changer on its hands. IBM’s new image recognition record of 33.8% accuracy in 7 hours handily surpasses the previous industry record set by Microsoft—29.9% accuracy in 10 days. To top it all off, IBM says DDL scales efficiently—across up to 256 GPUs, with up to 95% efficiency on the Caffe deep learning framework.
Now available in beta
Developers won’t have to wait to try out this new technology. IBM research is delivering a beta version of the DDL to IBM Systems, which is available now in the newly announced 4th revision of IBM’s PowerAI (for TensorFlow and Caffe, with Torch and Chainer to follow soon). I think this will be a great addition to IBM’s Power systems, which I’ve called the “Swiss Army knives of acceleration”—standard PCI express, CAPI, and NVLink, all wrapped up in one platform.
Another unique thing of note about DDL is that it will be available not only on-prem but also through the cloud—via a cloud provider called Nimbix. In today’s hybrid environment, this flexibility is obviously a plus. Developers can try it out beta version now on Nimbix, or on an IBM Power Systems server.
Wrapping up
One of the most interesting things for me is that this new technology is coming from IBM, not one of the flashier, louder AI proponents like Google or Facebook. It looks like if IBM can continue to bring “firsts” to the table, IBM is really shaping up to be not just a major player in the enterprise, but for deep learning overall. DDL and OpenPOWER are the secret sauce that I think will give IBM an edge it needs—significantly cutting down training times, and improving accuracy and efficiency. I’ll continue to watch with interest, but I think by getting rid of this bottleneck, DDL has the potential to really open the deep learning floodgates. It could be a real game-changer for IBM, PowerAI, and OpenPOWER.
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Apple joining the chip race
started by Arthur Hanson on -
Sam Altman says helpful agents are poised to become AI’s killer function
latest reply by Irish70 onstarted by Daniel Nenni on -
US moves to bar Huawei, other Chinese telecoms from certifying wireless equipment
latest reply by tooLongInEDA onstarted by Daniel Nenni on -
How to Build a $20 Billion Semiconductor Fab
latest reply by Barnsley onstarted by Daniel Nenni on -
TSMC’s Advanced Packaging Capacity is fully booked for next two years by Nvidia and AMD
latest reply by hskuo onstarted by Daniel Nenni on -
Apple M4 chip rumors: Everything you need to know
latest reply by siliconbruh999 onstarted by Daniel Nenni on -
Japan's Rapidus fab to compete with TSMC, Samsung for 2nm chips
latest reply by MKWVentures onstarted by Daniel Nenni on -
CHIPS for America Announces $285 million Funding Opportunity for a Digital Twin and Semiconductor CHIPS Manufacturing USA Institute
started by Daniel Nenni on -
Legacy Chip Overcapacity in China: Myth and Reality
latest reply by Jert onstarted by Daniel Nenni on -
TSMC dominates global foundry market with 61% market share in 4Q23, says Counterpoint
latest reply by Xebec onstarted by Daniel Nenni on
Recent Forum Threads
Recent Article Comments
Huawei’s and SMIC’s Requirement for 5nm Production: Improving Multipatterning Productivity
I really like that second article Fred, thanks for sharing! This being more of an alternate history of sorts is…— nghanayem on May 6, 2024Will my High-Speed Serial Link Work?
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Interesting part about dry resist cost and throughput. It was considered even before EUV many nodes ago.— Fred Chen on May 4, 2024Real men have fabs!
Hi Steve We include more Chinese companies in our data. As a consultancy, I hope you understand that we keep…— Claus Aasholm on April 28, 2024Real men have fabs!
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We track many more companies than what you see in the chart. Our insight model requires input data and Siemens…— Claus Aasholm on April 24, 2024Real men have fabs!
Under EDA you should add Siemens, Keysight EDA, etc.— Daniel Payne on April 24, 2024
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Podcast EP220: The Impact IQE’s Compound Semiconductors Are Having on the Industry with Dr. Rodney Pelzel
Podcast EP219: How Synopsys Addresses Debug and Coverage Closure Challenges with Robert Ruiz
Podcast EP218: How Dassault Systémes is Helping to Create the Workforce of the Future with Bill DeVries
EP217: The Impact and Unique Business Model of Silicon Creations with Randy Caplan
Podcast EP216: Q4 2023 is Another Strong Growth Quarter for EDA as Reviewed by Wally Rhines
Podcast EP215: A Tour of the GlobalFoundries Silicon Photonics Platform with Vikas Gupta
Podcast EP214: The Broad Impact of proteanTecs with Noam Brousard
Podcast EP213: The Impact of Arteris on Automotive and Beyond with Frank Schirrmeister
Podcast EP212: A View of the RISC-V Landscape with Synopsys’ Matt Gutierrez
Why NA is Not Relevant to Resolution in EUV Lithography