Neurogrid Circuit Board 9,000 Times Faster
Synopsis: 9,000 times faster energy-efficient microchips based on the human brain could drive prosthetic limbs with the speed and complexity of our actions. 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. 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.
- Neurogrid is a piece of computer hardware designed specifically to simulate biological brains. It uses analog computation to emulate ion channel activity and digital communication to softwire structured connectivity patterns. Neurogrid simulates one million neurons and six billion synapses in real-time. The neurons spike at a rate of ten times a second. Regarding the number of simulated neurons, it rivals the Blue Brain Project simulations. Neurogrid was designed and built by the "Brains in Silicon" group at Stanford University.
Stanford scientists 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 actions.
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, but it also 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 the 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 a Neurocore-like chip would control.
"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 neuro-compiler so that you would not need to know anything about synapses and neurons to able to use one of these."
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 Neuro-technologies - 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, attempting 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 many synaptic connections. This feature 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 expand that hardware base greatly.
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 the most cost-effective way to simulate neurons, 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 costs 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 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 the 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 reconfigurability 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.
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This quality-reviewed article relating to our Prostheses - Prosthetics section was selected for publishing by the editors of Disabled World due to its likely interest to our disability community readers. Though the content may have been edited for style, clarity, or length, the article "Neurogrid Circuit Board 9,000 Times Faster" was originally written by Stanford University, and published by Disabled-World.com on 2014-04-28 (Updated: 2022-06-28). Should you require further information or clarification, Stanford University can be contacted at stanford.edu. Disabled World makes no warranties or representations in connection therewith.
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