[ExI] cool! locals create circuit board modeled on the human brain

Thu May 1 15:35:14 UTC 2014

I read where the human brain is firing at a rate of four quadrillion time a
second.  Of course we don't want a computer to do all that our brain is
doing but still.......

On Tue, Apr 29, 2014 at 9:47 AM, spike <spike66 at att.net> wrote:

>
>
> I don't know how much of this is the usual hype that sticks to this topic
> like moss on an oak, but if I find more info I will post it here.  I hope
> we can create some kind of standard specialized device such as the
> Neurogrid which would allow the geek masses to experiment.  spike
>
>
> http://news.stanford.edu/pr/2014/pr-neurogrid-boahen-engineering-042814.html
>
> April 28, 2014
>
> Stanford bioengineers create circuit board modeled on the human brain
>
> Stanford bioengineers have developed faster, more energy-efficient
> microchips based on the human brain – 9,000 times faster and using
> significantly less power than a typical PC. This offers greater
> possibilities for advances in robotics and a new way of understanding the
> brain. For instance, a chip as fast and efficient as the human brain could
> drive prosthetic limbs with the speed and complexity of our own actions.
>
> BY TOM ABATE
>
> The Neurogrid circuit board can simulate orders of magnitude more neurons
> and synapses than other brain mimics on the power it takes to run a tablet
> computer.  Stanford bioengineers have developed a new circuit board modeled
> on the human brain, possibly opening up new frontiers in robotics and
> computing.
>
> For all their sophistication, computers pale in comparison to the brain.
> The modest cortex of the mouse, for instance, operates 9,000 times faster
> than a personal computer simulation of its functions.
>
> Not only is the PC slower, it takes 40,000 times more power to run, writes
> Kwabena Boahen, associate professor of bioengineering at Stanford, in an
> article for the Proceedings of the IEEE.
>
> "From a pure energy perspective, the brain is hard to match," says Boahen,
> whose article surveys how "neuromorphic" researchers in the United States
> and Europe are using silicon and software to build electronic systems that
> mimic neurons and synapses.
>
> Boahen and his team have developed Neurogrid, a circuit board consisting of
> 16 custom-designed "Neurocore" chips. Together these 16 chips can simulate
> 1 million neurons and billions of synaptic connections. The team designed
> these chips with power efficiency in mind. Their strategy was to enable
> certain synapses to share hardware circuits. The result was Neurogrid – a
> device about the size of an iPad that can simulate orders of magnitude more
> neurons and synapses than other brain mimics on the power it takes to run a
> tablet computer.
>
> The National Institutes of Health funded development of this
> million-neuron prototype with a five-year Pioneer Award. Now Boahen stands
> ready for the next steps – lowering costs and creating compiler software
> that would enable engineers and computer scientists with no knowledge of
> neuroscience to solve problems – such as controlling a humanoid robot –
> using Neurogrid.
>
> Its speed and low power characteristics make Neurogrid ideal for more than
> just modeling the human brain. Boahen is working with other Stanford
> scientists to develop prosthetic limbs for paralyzed people that would be
> controlled by a Neurocore-like chip.
>
> "Right now, you have to know how the brain works to program one of these,"
> said Boahen, gesturing at the $40,000 prototype board on the desk of his
> Stanford office. "We want to create a neurocompiler so that you would not
> need to know anything about synapses and neurons to able to use one of
> these."
>
> Brain ferment
>
> In his article, Boahen notes the larger context of neuromorphic research,
> including the European Union's Human Brain Project, which aims to simulate
> a human brain on a supercomputer. By contrast, the U.S. BRAIN Project –
> short for Brain Research through Advancing Innovative Neurotechnologies –
> has taken a tool-building approach by challenging scientists, including
> many at Stanford, to develop new kinds of tools that can read out the
> activity of thousands or even millions of neurons in the brain as well as
> write in complex patterns of activity.
>
> Zooming from the big picture, Boahen's article focuses on two projects
> comparable to Neurogrid that attempt to model brain functions in silicon
> and/or software.
>
> One of these efforts is IBM's SyNAPSE Project – short for Systems of
> Neuromorphic Adaptive Plastic Scalable Electronics. As the name implies,
> SyNAPSE involves a bid to redesign chips, code-named Golden Gate, to
> emulate the ability of neurons to make a great many synaptic connections –
> a feature that helps the brain solve problems on the fly. At present a
> Golden Gate chip consists of 256 digital neurons each equipped with 1,024
> digital synaptic circuits, with IBM on track to greatly increase the
> numbers of neurons in the system.
>
> Heidelberg University's BrainScales project has the ambitious goal of
> developing analog chips to mimic the behaviors of neurons and synapses.
> Their HICANN chip – short for High Input Count Analog Neural Network –
> would be the core of a system designed to accelerate brain simulations, to
> enable researchers to model drug interactions that might take months to
> play out in a compressed time frame. At present, the HICANN system can
> emulate 512 neurons each equipped with 224 synaptic circuits, with a
> roadmap to greatly expand that hardware base.
>
> Each of these research teams has made different technical choices, such as
> whether to dedicate each hardware circuit to modeling a single neural
> element (e.g., a single synapse) or several (e.g., by activating the
> hardware circuit twice to model the effect of two active synapses). These
> choices have resulted in different trade-offs in terms of capability and
> performance.
>
> In his analysis, Boahen creates a single metric to account for total
> system cost – including the size of the chip, how many neurons it simulates
> and the power it consumes.
>
> Neurogrid was by far the most cost-effective way to simulate neurons, in
> keeping with Boahen's goal of creating a system affordable enough to be
> widely used in research.
>
> Speed and efficiency
>
> But much work lies ahead. Each of the current million-neuron Neurogrid
> circuit boards cost about $40,000. Boahen believes dramatic cost reductions
> are possible. Neurogrid is based on 16 Neurocores, each of which supports
> 65,536 neurons. Those chips were made using 15-year-old fabrication
> technologies.
>
> By switching to modern manufacturing processes and fabricating the chips
> in large volumes, he could cut a Neurocore's cost 100-fold – suggesting a
> million-neuron board for $400 a copy. With that cheaper hardware and
> compiler software to make it easy to configure, these neuromorphic systems
> could find numerous applications.
>
> For instance, a chip as fast and efficient as the human brain could drive
> prosthetic limbs with the speed and complexity of our own actions – but
> without being tethered to a power source. Krishna Shenoy, an electrical
> engineering professor at Stanford and Boahen's neighbor at the
> interdisciplinary Bio-X center, is developing ways of reading brain signals
> to understand movement. Boahen envisions a Neurocore-like chip that could
> be implanted in a paralyzed person's brain, interpreting those intended
> movements and translating them to commands for prosthetic limbs without
> overheating the brain.
>
> A small prosthetic arm in Boahen's lab is currently controlled by
> Neurogrid to execute movement commands in real time. For now it doesn't
> look like much, but its simple levers and joints hold hope for robotic
> limbs of the future.
>
> Of course, all of these neuromorphic efforts are beggared by the
> complexity and efficiency of the human brain.
>
> In his article, Boahen notes that Neurogrid is about 100,000 times more
> energy efficient than a personal computer simulation of 1 million neurons.
> Yet it is an energy hog compared to our biological CPU.
>
> "The human brain, with 80,000 times more neurons than Neurogrid, consumes
> only three times as much power," Boahen writes. "Achieving this level of
> energy efficiency while offering greater configurability and scale is the
> ultimate challenge neuromorphic engineers face."
>
> Tom Abate writes about the students, faculty and research of the School of
> Engineering. Amy Adams of Stanford University Communications contributed to
> this report.
>
> For more Stanford experts in bioengineering and other topics, visit
> Stanford Experts.
>
>
>
> _______________________________________________
> extropy-chat mailing list
> extropy-chat at lists.extropy.org
> http://lists.extropy.org/mailman/listinfo.cgi/extropy-chat
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.extropy.org/pipermail/extropy-chat/attachments/20140501/e87a016e/attachment.html>