As we pointed out earlier this week, we’re still far from being able to replicate the awesome power of the human brain. So rather than use traditional models of computing, IBM has decided to design an entirely new computer architecture — one that’s taking inspiration from nature.


For nearly 70 years, computer scientists have depended upon the Von Neumann architecture. The computer that you’re working on right now still uses this paradigm — an electronic digital system driven by processors and consisting of various processing units, including an arithmetic logic unit, a control unit, memory, and input/output mechanisms. These separate units store and process information sequentially, and they use programming languages designed specifically for those architectures.


But the human brain, which most certainly must be a kind of computer, works a lot differently. It’s a massively parallel, massively redundant “computer” capable of generating approximately 1016 processes per second. It’s doubtful that it’s as serialized as the Von Neumann model. Nor is it driven by a proprietary programming language (though, as many cognitive scientists would argue, it’s likely driven by biologically encoded algorithms). Instead, the brain’s neurons and synapses store and process information in a highly distributed, parallel way.

Which is exactly how IBM’s new programming language, called Corelet, works as well. The company disclosed its plans at the the International Joint Conference on Neural Networks held this week in Dallas.


Credit: IBM.

Researchers from IBM are working on a new software front-end for their neuromorphic processor chips. The company is hoping to draw inspiration from its recent successes in “cognitive computing,” a line of R&D that’s best exemplified by Watson, the Jeopardy-playing AI. The new programming language will be necessary because once IBM’s cognitive computers become a reality, they’ll need a completely new one to run them. Many of today’s computers still use programming derived from FORTRAN, a language developed in the 1950s for ENIAC.


The new software runs on a conventional supercomputer, but it simulates the functioning of a massive network of neurosynaptic cores. Each core contains its own network of 256 neurons which function according to a new model in which digital neurons mimic the independent nature of biological neurons. Corelets, the equivalent of “programs,” specify the basic functioning of neurosynaptic cores and can be linked into more complex structures. Each corelet has 256 outputs and inputs, which are used to connect to one another.


“Traditional architecture is very sequential in nature, from memory to processor and back,” explained Dr. Dharmendra Modha in a recent Forbes article. “Our architecture is like a bunch of LEGO blocks with different features. Each corelet has a different function, then you compose them together.”

So, for example, a corelet can detect motion, the shape of an object, or sort images by color. Each corelet would run slowly, but the processing would be in parallel.


IBM has created more than 150 corelets as part of a library that programmers can tap.

Eventually, IBM hopes to create a cognitive computer scaled to 100 trillion synapses.


But there are limits to the proposed technology. Alex Knapp explains:

Of course, even those hybrid computers won’t be a replacement for the human brain. The IBM chips and architecture may be inspired by the human brain, but they don’t quite operate like it. “We can’t build a brain,” Dr. Modha told me. “But the world is being populated every day with data. What we want to do is to make sense of that data and extract value from it, while staying true to what can be build on silicon. We believe that we’ve found the best architecture to do that in terms of power, speed and volume to get as close as we can to the brain while remaining feasible.”


Corelet could enable the next generation of intelligent sensor networks that mimic the brain’s abilities for perception, action, and cognition.

More in this video:

And the IBM white paper can be found here.

[Via MIT Technology Review, Forbes, IBM]