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Title    The Second Coming of Neuromorphic Computing
Date    Friday February 12 2016, @01:54AM
Author    CoolHand
Topic   
from the making-smart-computers dept.
https://soylentnews.org/article.pl?sid=16/02/11/1415253

takyon writes:

The Next Platform has an article about waning interest in brain-inspired neuromorphic computing post-2013 (which has not yet delivered a "revolution in computing") and some of the developments in the field since then:

There have been a couple of noteworthy investments that have fed existing research for neuromorphic architectures. The DARPA Synapse program was one such effort, which beginning in 2008, eventually yielded IBM's "True North" chip—a 4096-core device comprised of 256 programmable "neurons" that act much like synapses in the brain, resulting in a highly energy efficient architecture that while fascinating—means an entire rethink of programming approaches. Since that time, other funding from scientific sources, including the Human Brain Project, have pushed the area further, leading to the creation of the SpiNNaker neuromorphic device, although there is still a lack of a single architecture that appears best for neuromorphic computing in general.

The problem is really that there is no "general" purpose for such devices as of yet and no widely accepted device or programmatic approach. Much of this stems from the fact that many of the existing projects are built around specific goals that vary widely. For starters, there are projects around broader neuromorphic engineering that are more centered on robotics versus large-scale computing applications (and vice versa). One of several computing-oriented approaches taken by Stanford University's Neurogrid project, which was presented in hardware in 2009 and remains an ongoing research endeavor, was to simulate the human brain, thus the programming approach and hardware design are both thus modeled as closely to the brain as possible while others are more oriented toward solving computer science related challenges related to power consumption and computational capability using the same concepts, including a 2011 effort at MIT, work at HP with memristors as a key to neuromorphic device creation, and various other smaller projects, including one spin-off of the True North architecture we described here.

[more]

[...] "Neuromorphic computing is still in its beginning stages," Dr. Catherine Schuman, a researcher working on such architectures at Oak Ridge National Laboratory tells The Next Platform. "We haven't nailed down a particular architecture that we are going to run with. True North is an important one, but there are other projects looking at different ways to model a neuron or synapse. And there are also a lot of questions about how to actually use these devices as well, so the programming side of things is just as important."

The programming approach varies from device to device, as Schuman explains. "With True North, for example, the best results come from training a deep learning network offline and moving that program onto the chip. Others that are biologically inspired implementations like Neurogrid, for instance, are based on spike timing dependent plasticity."

The approach Schuman's team is working on at Oak Ridge and the University of Tennessee is based on a neuromorphic architecture called NIDA, short for the Neuroscience Inspired Dynamic Architecture, which was implemented in FPGA in 2014 and now has a full SDK and tooling around it. The hardware implementation, called Dynamic Adaptive Neural Network Array (DANNA) differs from other approaches to neuromorphic computing in that is allows for programmability of structure and is trained using an evolutionary optimization approach—again, based as closely as possible to what we know (and still don't know) about the way our brains work.

Schuman stresses the exploratory nature of existing neuromorphic computing efforts, including those at the lab, but does see a new host of opportunities for them on the horizon, presuming the programming models can be developed to suit both domain scientists and computer scientists. There are, she notes, two routes for neuromorphic devices in the next several years. First, as embedded processors on sensors and other devices, given their low power consumption and high performance processing capability. Second, and perhaps more important for a research center like Oak Ridge National Lab, neuromorphic devices could act "as co-processors on large-scale supercomputers like Titan today where the neuromorphic processor would sit alongside the traditional CPUs and GPU accelerators." Where they tend to shine most, and where her team is focusing effort, is on the role they might play in real-time data analysis.

Also mentioned: Qualcomm's Zeroth cognitive computing platform and their support for the efforts of Brain Corporation.


Original Submission

Links

  1. "takyon" - https://soylentnews.org/~takyon/
  2. "waning interest in brain-inspired neuromorphic computing post-2013" - http://www.nextplatform.com/2016/02/09/the-second-coming-of-neuromorphic-computing/
  3. "SpiNNaker" - https://www.humanbrainproject.eu/neuromorphic-computing-platform1
  4. "Neurogrid" - http://web.stanford.edu/group/brainsinsilicon/goals.html
  5. "2011 effort at MIT" - http://news.mit.edu/2011/brain-chip-1115
  6. "memristors as a key" - https://www.ece.ucsb.edu/~strukov/papers/2013/NatNano2013.pdf
  7. "we described here" - http://www.nextplatform.com/2015/04/14/old-darpa-technology-to-push-new-supercomputer-performance/
  8. "Dynamic Adaptive Neural Network Array" - https://web.eecs.utk.edu/~plank/plank/papers/2015-Schuman-SC-Workshop.pdf
  9. "Zeroth cognitive computing platform" - https://www.qualcomm.com/invention/cognitive-technologies/zeroth
  10. "Brain Corporation" - http://www.braincorporation.com/
  11. "Original Submission" - https://soylentnews.org/submit.pl?op=viewsub&subid=12107

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