• An OS for Neuromorphic Computing on Von Neumann Devices

    August 2, 2017

    Ziyang Xu from Peking University in Beijing sees several similarities between the human brain and Von Neumann computing devices.

    While he believes there is value in neuromorphic, or brain-inspired, chips, with the right operating system, standard processors can mimic some of the efficiencies of the brain and achieve similar performance for certain tasks.

    In short, even though our brains do not have the same high-speed, high-frequency capacity of modern chips, the way information is routed and addressed is the key. At the core of this efficiency is a concept similar to a policy engine governing information compression, storage, and retrieval. …

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  • U.S. Military Sees Future in Neuromorphic Computing

    June 26, 2017

    The novel architectures story is still shaping out for 2017 when it comes machine learning, hyperscale, supercomputing and other areas.

    From custom ASICs at Google, new uses for quantum machines, FPGAs finding new routes into wider application sets, advanced GPUs primed for deep learning, hybrid combinations of all of the above, it is clear there is serious exploration of non-CMOS devices. When the Department of Energy in the U.S. announced its mission to explore novel architectures, one of the clear candidates for investment appeared to be neuromorphic chips—efficient pattern matching devices that are in development at Stanford (NeuroGrid), The …

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  • Thinking Through The Cognitive HPC Nexus With Big Blue

    June 21, 2017

    There are plenty of things that the members of the high performance community do not agree on, there is a growing consensus that machine learning applications will at least in some way be part of the workflow at HPC centers that do traditional simulation and modeling.

    Some HPC vendors think the HPC and AI systems have either already converged or will soon do so, and others think that the performance demands (both in terms of scale and in time to result) on both HPC and AI will necessitate radically different architectures and therefore distinct systems for these two workloads. IBM, …

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  • Neuromorphic, Quantum, Supercomputing Mesh for Deep Learning

    March 29, 2017

    It is difficult to shed a tear for Moore’s Law when there are so many interesting architectural distractions on the systems horizon.

    While the steady tick-tock of the tried and true is still audible, the last two years have ushered a fresh wave of new architectures targeting deep learning and other specialized workloads, as well as a bevy of forthcoming hybrids with FPGAs, zippier GPUs, and swiftly emerging open architectures. None of this has been lost on system architects at the bleeding edge, where the rush is on to build systems that can efficiently chew through ever-growing datasets with …

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  • Stanford Brainstorm Chip to Hints at Neuromorphic Computing Future

    March 27, 2017

    If the name Kwabena Boahen sounds familiar, you might remember silicon that emerged in the late 1990s that emulated the human retina.

    This retinomorphic vision system, which Boahen developed while at Caltech under VLSI and neuromorphic computing pioneer, Carver Mead, introduced ideas that are just coming around into full view again in the last couple of years—computer vision, artificial intelligence, and of course, brain-inspired architectures that route for efficiency and performance. The rest of his career has been focused on bringing bioinspired engineering to a computing industry that is hitting a major wall in coming years—and at a time …

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  • Memristor Research Highlights Neuromorphic Device Future

    February 15, 2017

    Much of the talk around artificial intelligence these days focuses on software efforts – various algorithms and neural networks – and such hardware devices as custom ASICs for those neural networks and chips like GPUs and FPGAs that can help the development of reprogrammable systems. A vast array of well-known names in the industry – from Google and Facebook to Nvidia, Intel, IBM and Qualcomm – is pushing hard in this direction, and those and other organizations are making significant gains thanks to new AI methods as deep learning.

    All of this development is happening at a time when the …

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  • Intel Gets Serious About Neuromorphic, Cognitive Computing Future

    February 11, 2017

    Like all hardware device makers eager to meet the newest market opportunity, Intel is placing multiple bets on the future of machine learning hardware. The chipmaker has already cast its Xeon Phi and future integrated Nervana Systems chips into the deep learning pool while touting regular Xeons to do the heavy lifting on the inference side.

    However, a recent conversation we had with Intel turned up a surprising new addition to the machine learning conversation—an emphasis on neuromorphic devices and what Intel is openly calling “cognitive computing” (a term used primarily—and heavily—for IBM’s Watson-driven AI technologies). This is the first …

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  • The Second Coming of Neuromorphic Computing

    February 9, 2016

    Just a few years ago, the promise of ultra-low power, high performance computing was tied to the rather futuristic-sounding vision of a “brain chip” or neuromorphic processor, which could mimic the brain’s structure and processing ability in silicon—quickly learning and chewing on data as fast as it could be generated.

    In that short amount of time, broader attention has shifted to other devices and software frameworks for achieving the same end. From custom ASICs designed to train and execute neural networks, to reprogrammable hardware with a low-power hook, like FPGAs, to ARM, GPUs, and other non-standard CPU …

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