In November 2012, IBM announced that it had used the Blue Gene/Q Sequoia supercomputer to achieve an unprecedented simulation of more than 530 billion neurons. The Blue Gene/Q Sequoia accomplished this feat thanks to its blazing fast speed; it clocks in at over 16 quadrillion calculations per second. In fact, it currently ranks as the second-fastest supercomputer in the world. But, according to Kwabena Boahen, Ph.D., the Blue Gene still doesn’t compare to the computational power of the brain itself.
“The brain is actually able to do more calculations per second than even the fastest supercomputer,” says Boahen, a professor at Stanford University, director of the Brains in Silicon research laboratory and an NSF Faculty Early Career grant recipient.
That’s not to say the brain is faster than a supercomputer. In fact, it’s actually much slower. The brain can do more calculations per second because it’s “massively parallel,” meaning networks of neurons are working simultaneously to solve a great number of problems at once. Traditional computing platforms, no matter how fast, operate sequentially, meaning each step must be complete before the next step is begun.
Boahen works at the forefront of a field called neuromorphic engineering, which seeks to replicate the brain’s extraordinary computational abilities using innovative hardware and software applications. His laboratory’s most recent accomplishment is a new computing platform called Neurogrid, which simulates the activity of 1 million neurons.
Neurogrid is not a supercomputer. It can’t be used to simulate the big bang, or forecast hurricanes, or predict epidemics. But what it can do sets it apart from any computational platform on earth. Neurogrid is the first simulation platform that can model a million neurons in real time. As such, it represents a powerful tool for investigating the human brain. In addition to providing insight into the normal workings of the brain, it has the potential to shed light on complex brain diseases like autism and schizophrenia, which have so far been difficult to model. Via Energy efficient brain simulator outperforms supercomputers.