• Deep Learning Architectures Hinge on Hybrid Memory Cube

    September 12, 2016

    We have heard about a great number of new architectures and approaches to scalable and efficient deep learning processing that sit outside of the standard CPU, GPU, and FPGA box and while each is different, many are leveraging a common element at all-important memory layer.

    The Hybrid Memory Cube (HMC), which we expect to see much more of over the coming year and beyond, is at the heart of several custom architectures to suit the deep learning market. Nervana Systems, which was recently acquired by Intel (HMC maker, Micron’s close partner), Wave Computing, and other research efforts all see a …

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  • IARPA Project Targets Hidden Algorithms of the Brain

    February 10, 2016

    Whether in the brain or in code, neural networks are shaping up to be one of the most critical areas of research in both neuroscience and computer science. An increasing amount of attention, funding, and development has been pushed toward technologies that mimic the brain in both hardware and software to create more efficient, high performance systems capable of advanced, fast learning.

    One aspect of all the efforts toward more scalable, efficient, and practical neural networks and deep learning frameworks we have been tracking here at The Next Platform is how such systems might be implemented in research and enterprise …

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  • Emergent Chip Vastly Accelerates Deep Neural Networks

    December 8, 2015

    Stanford University PhD candidate, Song Han, who works under advisor and networking pioneer, Dr. Bill Dally, responded in a most soft-spoken and thoughtful way to the question of whether the coupled software and hardware architecture he developed might change the world.

    In fact, instead of answering the question directly, he pointed to the range of applications, both in the present and future, that will be driven by near real-time inference for complex deep neural networks—all a roundabout way of showing not just why what he is working toward is revolutionary, but why the missing pieces he is filling in have …

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  • GPU Platforms Set to Lengthen Deep Learning Reach

    December 7, 2015

    For Google, Baidu, and a handful of other hyperscale companies that have been working with deep neural networks and advanced applications for machine learning well ahead of the rest of the world, building clusters for both the training and inference portions of such workloads is kept, for the most part, a well-guarded secret. What is clear from what we have been able to gather, however, is that GPUs are the key to efficiently accelerating training neural networks—a process that is time-consuming and computationally intensive.

    GPU maker, Nvidia, has been on the front end of the push to continue this trend. …

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  • Why The Golden Age Of Machine Learning is Just Beginning

    October 20, 2015

    Even though the buzz around neural networks, artificial intelligence, and machine learning has been relatively recent, as many know, there is nothing new about any of these methods. If so many of the core algorithms and approaches have been around for decades, why is it just now that they are getting their day in the sun?

    To answer that question, we can take a look at what has happened over the last five years or so with the attention and tooling around data. And we can also point to the dramatic increase in scalable compute power, or to be …

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  • The Black Box Problem Closes in on Neural Networks

    September 7, 2015

    Explaining the process of how any of us might have arrived to a particular conclusion or decision by verbally detailing the variables, weights, and conditions that our brains navigate through to arrive at an answer can be complex enough. But what happens when we ask a neural network, which itself is based on the loose architecture of our own brains, how it arrived at a particular conclusion?

    In neural networks and artificial intelligence research, the difficulty for the system to provide a suitable explanation for how it arrived at an answer is referred to as “the black box problem.” Just …

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