• An Early Look at Baidu’s Custom AI and Analytics Processor

    August 22, 2017

    In the U.S. it is easy to focus on our native hyperscale companies (Google, Amazon, Facebook, etc.) and how they design and deploy infrastructure at scale.

    But as our regular readers understand well, the equivalent to Google in China, Baidu, has been at the bleeding edge with chips, systems, and software to feed its own cloud-delivered and research operations.

    We’ve written much over the last few years about the company’s emphasis on streamlining deep learning processing, most notably with GPUs, but Baidu has a new processor up its sleeve called the XPU. For now, the device has just been demonstrated …

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  • A Deep Learning Performance Lens for Low Precision Inference

    June 28, 2017

    Few companies have provided better insight into how they think about new hardware for large-scale deep learning than Chinese search giant, Baidu.

    As we have detailed in the past, the company’s Silicon Valley Research Lab (SVAIL) in particular has been at the cutting edge of model development and hardware experimentation, some of which is evidenced in their publicly available (and open source) DeepBench deep learning benchmarking effort, which allowed users to test different kernels across various hardware devices for training.

    Today, Baidu SVAIL extended DeepBench to include support for inference as well as expanded training kernels. Also of …

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  • Baidu Targets Deep Learning Scalability Challenges

    February 22, 2017

    When it comes to solving deep learning cluster and software stack problems at scale, few companies are riding the bleeding edge like Chinese search giant, Baidu. As we have detailed in the past, the company’s Silicon Valley AI Lab (SVAIL) has some unique hardware and framework implementations that put AI to the test at scale. As it turns out, scalability of the models they specialize in (beginning with speech recognition) is turning out to be one of the great challenges ahead on all fronts—hardware, compiler/runtime, and framework alike.

    As we have described across multiple use cases, at Baidu and elsewhere …

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  • Baidu’s New Yardstick for Deep Learning Hardware Makers

    September 26, 2016

    When it comes to deep learning innovation on the hardware front, few other research centers have been as forthcoming with their results as Baidu. Specifically, the company’s Silicon Valley AI Lab (SVAIL) has been the center of some noteworthy work on GPU-based deep learning as well as exploratory efforts using novel architectures specifically for ultra-fast training and inference.

    It stands to reason that teams at SVAIL don’t simply throw hardware at the wall to see what sticks, even though they seem to have more to toss around than most. Over the last couple of years, they have broken down …

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  • Baidu Takes FPGA Approach to Accelerating SQL at Scale

    August 24, 2016

    While much of the work at Baidu we have focused on this year has centered on the Chinese search giant’s deep learning initiatives, many other critical, albeit less bleeding edge applications present true big data challenges.

    As Baidu’s Jian Ouyang detailed this week at the Hot Chips conference, Baidu sits on over an exabyte of data, processes around 100 petabytes per day, updates 10 billion webpages daily, and handles over a petabyte of log updates every 24 hours. These numbers are on par with Google and as one might imagine, it takes a Google-like approach to problem solving at …

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  • Baidu Eyes Deep Learning Strategy in Wake of New GPU Options

    April 22, 2016

    This month Nvidia bolstered its GPU strategy to stretch further into deep learning, high performance computing, and other markets, and while there are new options to consider, particularly for the machine learning set, it is useful to understand what these new arrays of chips and capabilities mean for users at scale. As one of the companies directly in the lens for Nvidia with its recent wave of deep learning libraries and GPUs, Baidu has keen insight into what might tip the architectural scales—and what might still stay the same, at least for now.

    Back in December, when we talked to …

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  • Baidu’s Chief Scientist on Intersection of Supercomputing, Machine Learning

    April 1, 2016 Nages Sieslack

    “AI is transforming the entire world of technology. Much of this progress is due to the ability of learning algorithms to spot patterns in larger and larger amounts of data. Today this is powering everything from web search to self-driving cars. This insatiable hunger for processing data has caused the bleeding edge of machine learning to shift from CPU computing, to cloud, to GPU, to HPC,” observes Andrew Ng, the Chief Scientist at Baidu.

    Ng will describe this in more detail at his much-anticipated upcoming talk, How HPC in Supercharging Machine Learning  at the June ISC High Performance conference in …

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  • Baidu Looks to Next Generation Deep Learning Accelerators

    January 21, 2016

    If the last year of stories here from research labs at the forefront of deep learning hasn’t made it clear, the accelerator of choice for training the models that will feed the next generation of speech and image recognition (not to mention a wealth of other application areas) is certainly GPUs.

    However, if there is something else that should be clear from reading, the next best thing is always around the corner—and companies whose business it is to be the most efficient, highest performance, and cost effective with their computations are always keeping an eye open for what’s around the …

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  • Inside the GPU Clusters that Power Baidu’s Neural Networks

    December 11, 2015

    While many consumers in the U.S. might not have heard much about Baidu, when it comes to engineers and computer scientists, the Chinese company is on par with Google, Facebook, and their ilk when it comes to massively scaled distributed computing. It uses that iron to deliver software capability that requires such scale.

    Baidu has garnered significant recognition for pioneering efforts in machine learning and deep neural networks, which the company uses to deliver services including near real-time speech recognition across vastly different languages, as well as English to Mandarin translation (and vice versa). Like other hyperscale companies, the tough …

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