After six years, significant investment by DARPA and IBM, and millions of man hours, researchers at IBM have created two silicon chips that are fundamentally brain-like in their operation.
Built using IBM’s 45nm process, these chips are still constructed out of transistors, but they’re organized into a novel arrangement that mimics the neurons and synapses of animal brains. Both of the chips have 256 digital neurons that operate at 10MHz, and standard chip features like memory, communication controllers, and so on. One of the chips has 262,144 human-programmable synapses, which makes it something of a glorified (and rather dumb) FPGA — but the other chip has 65,356 learning synapses, and that’s what we’re more interested in.
IBM’s learning digital neurons can dynamically rewire their synapses based on their inputs, just like an animal brain. The neurons remember their recent activities — which synapse they triggered — and as these communication channels are used their weighting (importance) increases, just like a software neural network. With 256 neurons and 65,356 synapses in total, each neuron can make at least 255 connections with other neurons, for a total of almost 17 million different combinations.
As far as the actual architecture goes, IBM hasn’t intimated any more than the fact that the digital neurons are arranged in a crossbar array — a matrix of switches, much like a telephone exchange or packet-switched router. Instead of having inputs and outputs, though, the IBM chips will have a crossbar that connects each neuron to its 255 cousins. Crossbars are easy to implement, however — but because of their non-blocking nature (every neuron has one or more physical connections to every other neuron) they don’t scale gracefully. The result, according to IBM Research’s Dharmendra Modha is that these chips are capable of “massive, massive amounts of parallelism” — but whether this means that all 256 neurons can operate at the same time or not, who knows.
It’s also important to note that 256 neurons is incredibly dumb, as far as brains go — a pond snail has 11,000 — but the fact that IBM has done this using conventional silicon fabrication techniques is significant. Until now, almost every foray into artificial intelligence and brain simulation has been done in software on supercomputers, like Blue Brain — and supercomputers require huge amounts of space, money, and power. By shrinking a neural network onto a chip, IBM can reduce the power requirement to just a few watts — and if their design will scale, it might be possible to create millions or billions of digital neurons on a computer chip the size of… say… the human brain.
For now, IBM says these chips have successfully learnt their way around a maze and played Pong — and the next step, along with the creation of a few more neurons, is to task the chips with challenges that humans naturally excel at, like pattern recognition. Brain-like chips could be used to analyze image and video for recognizable objects, or to analyze and react to real-time data from weather stations. The main thing, according to IBM Research’s Dharmendra Modha
Ultimately, though, IBM’s goal has always been to marry the physical world and its infrastructure with information technology. The physical universe, thanks to us humans, tends to be random, chaotic, and generally very hard to understand as far as computers are concerned. Information technology, on the other hand, generally deals with numbers and data that can be readily manipulated by computers. IBM’s brain-like chips would create the perfect interface, perhaps spurring a new era of computer-controlled traffic signals, air traffic control, utilities, and more. Imagine outfitting IBM Watson with one of these digital chips, and the running/walking functionality of MABEL…
Read more at PCWorld