IF YOU have a smartphone, you probably have a slice of âs brain in your pocket. By the time you read this, his 1-billion-neuron silicon brain will be in production at a microchip plant in Taiwan.
Computer engineers have long wanted to copy the compact power of biological brains. But the best mimics so far have been impractical, being simulations running on supercomputers.
Furber, a computer scientist at the University of Manchester, UK, says that if we want to use computers with even a fraction of a brainâs flexibility, we need to start with affordable, practical, low-power components.
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âWeâre using bog-standard, off-the-shelf processors of fairly modest performance,â he says.
Furber wonât come close to copying every property of real neurons, says , head of . This is IBMâs attempt to simulate a brain with unsurpassed accuracy on a Blue Gene supercomputer at the Swiss Institute for Technology, Lausanne. âItâs a worthy aim, but brain-inspired chips can only produce brain-like functions,â he says.
Thatâs good enough for Furber, who wants to start teaching his brain-like computer about the world as soon as possible. His first goal is to teach it how to control a robotic arm, before working towards a design to control a humanoid. A robot controller with even a dash of brain-like properties should be much better at tasks like image recognition, navigation and decision-making, says Furber.
âRobots offer a natural, sensory environment for testing brain-like computers,â says Furber. âYou can instantly tell if it is being useful.â
Called â for Spiking Neural Network Architecture â the brain is based on a processor created in 1987 by Furber and colleagues at in Cambridge, UK, makers of the seminal .
Although the chip was made for a follow-up computer that flopped, the ARM design at its heart lived on, becoming the most common âembeddedâ processor in devices like e-book readers and smartphones.
But coaxing any computer into behaving like a brain is tough. Both real neurons and computer circuits communicate using electrical signals, but in biology the âwiresâ carrying them do not have fixed roles as in electronics. The importance of a particular neural connection, or synapse, varies as the network learns by balancing the influence of the different signals being received. This synaptic âweightingâ must be dynamic in a silicon brain, too.
âTo coordinate its âneuronsâ the chip mimics the way real neurons communicate using âspikesâ in voltageâ
The chips under construction in Taiwan contain 20 ARM processor cores, each modelling 1000 neurons. With 20,000 neurons per chip, 50,000 chips will be needed to reach the target of 1 billion neurons.
A memory chip next to each processor stores the changing synaptic weights as simple numbers that represent the importance of a given connection at any moment. Initially, those will be loaded from a PC, but as the system gets bigger and smarter, says Furber, âthe only computer able to compute them will be the machine itselfâ.
Another brain-like behaviour his chips need to master is to communicate coordinated âspikesâ of voltage. A computer has no trouble matching the speed at which individual neurons spike â about 10 times per second â but neurons work in very much larger, parallel groups than silicon logic gates.
In a brain there is no top-down control to coordinate their actions because the basic nature of individual neurons means that they work together in an emergent, bottom-up way.
Spinnaker cannot mimic that property, so it relies on a miniature controller to direct spike traffic, similar to one of the routers in the internetâs backbone. âWe can route to more than 4 billion neurons,â says Furber, âmany more than we need.â
While the Manchester team await the arrival of their chips, they have built a cut-down version with just 50 neurons and have put the prototype through its paces in the lab. They have created a virtual environment in which the silicon brain controls a Pac-Man-like program that learns to .
âIt shows that our four years designing the system havenât been wasted,â says Furber. He hopes to have a 10,000-processor version working later this year.
As they attempt to coax brain-like behaviour from phone chips, others are working with hardware which may have greater potential.
The , the Pentagonâs research arm, is funding a project called Synapse. Wei Lu of the University of Michigan at Ann Arbor, is working on a way of providing synaptic weights with memristors, first made in 2008 (51¶ŻÂț, 3 May 2008, p 26).
Handily, their most basic nature is brain-like: at any one moment a memristorâs resistance depends on the last voltage placed across it. This rudimentary âmemoryâ means that simple networks of memristors form weighted connections like those of neurons. This memory remains without drawing power, unlike the memory chips needed in Spinnaker. âMemristors are pretty neat,â says Lu.
Their downside is that they are untested, though. âSynapse is an extremely ambitious project,â says Furber. âBut ambition is what drives this field. No one knows the right way to go.â
