• Streamlining Medical Research With Machine Learning

    August 21, 2017 Vineeth Ram

    In this fast-paced global economy, enhanced speed, productivity, and intelligence are more important than ever to success. Machines are now being leveraged to augment human capabilities in order to drive business growth or accelerate innovation. Businesses need leading-edge IT to achieve superhuman levels of performance.

    Today’s enterprises and organizations are deploying high performance computing (HPC) technologies to reach the new frontier of IT intelligence. Backed by HPC solutions, users can leverage artificial intelligence (AI) tools to predict and solve problems in real time, streamline IT operations, and drive more informed, data-driven decision-making.

    Utilizing Innovative AI Tools

    Machine learning, …

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  • Nvidia Is A Textbook Case Of Sowing And Reaping Markets

    August 11, 2017

    In a properly working capitalist economy, innovative companies make big bets, help create new markets, vanquish competition or at least hold it at bay, and profit from all of the hard work, cleverness, luck, and deal making that comes with supplying a good or service to demanding customers.

    There is no question that Nvidia has become a textbook example of this as it helped create and is now benefitting from the wave of accelerated computing that is crashing into the datacenters of the world. The company is on a roll, and is on the very laser-sharp cutting edge of its …

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  • Fujitsu Bets On Deep Learning And HPC Divergence

    August 9, 2017

    One of the luckiest coincidences in the past decade has been that the hybrid machines designed for traditional HPC simulation and modeling workloads. which combine the serial processing performance of CPUs and the parallel processing and massive memory bandwidth of GPUs, were also well suited to run machine learning training applications.

    If the HPC community had not made the investments in hybrid architectures, the hyperscalers and their massive machine learning operations, which drive just about all aspects of their businesses these days, would not have seen such stellar results. (And had that not happened, many of us would have had …

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  • IBM Highlights PowerAI, OpenPower System Scalability

    August 9, 2017

    The golden grail of deep learning has two handles. On the one hand, developing and scaling systems that can train ever-growing model sizes is one concern. And on the other side, cutting down inference latencies while preserving accuracy of trained models is another issue.

    Being able to do both on the same system represents its own host of challenges, but for one group at IBM Research, focusing on the compute-intensive training element will have a performance and efficiency trickle-down effect that speed the entire deep learning workflow—from training to inference. This work, which is being led at the T.J. Watson …

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  • The Shape Of AMD HPC And AI Iron To Come

    August 8, 2017

    In the IT business, just like any other business, you have to try to sell what is on the truck, not what is planned to be coming out of the factories in the coming months and years. AMD has put a very good X86 server processor into the market for the first time in nine years, and it also has a matching GPU that gives its OEM and ODM partners a credible alternative for HPC and AI workload to the combination of Intel Xeons and Nvidia Teslas that dominate hybrid computing these days.

    There are some pretty important caveats to …

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  • Google Research Pushing Neural Networks Out of the Datacenter

    August 8, 2017

    Google has been at the bleeding edge of AI hardware development with the arrival of its TPU and other system-scale modifications to make large-scale neural network processing efficient and fast.

    But just as these developments come to fruition, advances in trimmed-down deep learning could move many more machine learning training and inference operations out of the datacenter and into your palm.

    Although it might be natural to think the reason that neural networks cannot be processed on devices like smartphones is because of limited CPU power, the real challenge lies in the vastness of the model sizes and hardware memory …

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  • Wrenching Efficiency Out of Custom Deep Learning Accelerators

    August 7, 2017

    Custom accelerators for neural network training have garnered plenty of attention in the last couple of years, but without significant software footwork, many are still difficult to program and could leave efficiencies on the table. This can be addressed through various model optimizations, but as some argue, the efficiency and utilization gaps can also be addressed with a tailored compiler.

    Eugenio Culurciello, an electrical engineer at Purdue University, argues that getting full computational efficiency out of custom deep learning accelerators is difficult. This prompted his team at Purdue to build an FPGA based accelerator that could be agnostic to CNN …

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  • Managing Deep Learning Development Complexity

    August 1, 2017

    For developers, deep learning systems are becoming more interactive and complex. From the building of more malleable datasets that can be iteratively augmented, to more dynamic models, to more continuous learning being built into neural networks, there is a greater need to manage the process from start to finish with lightweight tools.

    “New training samples, human insights, and operation experiences can consistently emerge even after deployment. The ability of updating a model and tracking its changes thus becomes necessary,” says a team from Imperial College London that has developed a library to manage the iterations deep learning developers make across …

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  • The Golden Grail: Automatic Distributed Hyperparameter Tuning

    July 17, 2017

    While it might not be an exciting problem front and center of AI conversations, the issue of efficient hyperparameter tuning for neural network training is a tough one. There are some options that aim to automate this process but for most users, this is a cumbersome area—and one that can lead to bad performance when not done properly.

    The problem with coming up with automatic tools for tuning is that many machine learning workloads are dependent on the dataset and the conditions of the problem being solved. For instance, some users might prefer less accuracy over a speedup or efficiency …

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  • The System Bottleneck Shifts To PCI-Express

    July 14, 2017

    No matter what, system architects are always going to have to contend with one – and possibly more – bottlenecks when they design the machines that store and crunch the data that makes the world go around. These days, there is plenty of compute at their disposal, a reasonable amount of main memory to hang off of it, and both Ethernet and InfiniBand are on the cusp of 200 Gb/sec of performance and not too far away from 400 Gb/sec and even higher bandwidths.

    Now, it looks like the peripheral bus based on the PCI-Express protocol is becoming the bottleneck, …

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