New computer chip modeled on a living brain can learn and remember

IBM, with help from DARPA, has built two working prototypes of a "neurosynaptic chip." Based on the neurons and synapses of the brain, these first-generation cognitive computing cores could represent a major leap in power, speed and efficiency

A pair of brain-inspired cognitive computer chips unveiled today could be a new leap forward — or at least a major fork in the road — in the world of computer architecture and artificial intelligence.

About a year ago, we told you about IBM’s project to map the neural circuitry of a macaque, the most complex brain networking project of its kind. Big Blue wasn’t doing it just for the sake of science — the goal was to reverse-engineer neural networks, helping pave the way to cognitive computer systems that can think as efficiently as the brain. Now they’ve made just such a system — two, actually — and they’re calling them neurosynaptic chips.

Built on 45 nanometer silicon/metal oxide semiconductor platform, both chips have 256 neurons. One chip has 262,144 programmable synapses and the other contains 65,536 learning synapses — which can remember and learn from their own actions. IBM researchers have used the compute cores for experiments in navigation, machine vision, pattern recognition, associative memory and classification, the company says. It’s a step toward redefining computers as adaptable, holistic learning systems, rather than yes-or-no calculators.

“This new architecture represents a critical shift away form today’s traditional von Neumann computers, to extremely power-efficient architecture,” Dharmendra Modha, project leader for IBM Research, said in an interview. “It integrates memory with processors, and it is fundamentally massively parallel and distributed as well as event-driven, so it begins to rival the brain’s function, power and space.”

You can read up on Von Neumann architecture over here, but essentially it is a system with two data portals, which are shared by the input instructions and output data. This creates a bottleneck that will fundamentally limit the speed of memory transfer. IBM’s system eliminates that bottleneck by putting the circuits for data computation and storage together, allowing the system to compute information from multiple sources at the same time with greater efficiency. Also like the brain, the chips have synaptic plasticity, meaning certain regions can be reconfigured to perform tasks to which they were not initially assigned.

IBM’s long-term goal is to build a chip system with 10 billion neurons and 100 trillion synapses that consumes just one kilowatt-hour of electricity and fits inside a shoebox, Modha said.

The project is funded by DARPA’s SyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) initiative, and IBM just completed phases 0 and 1. IBM’s project, which involves collaborators from Columbia University, Cornell University, the University of California-Merced and the University of Wisconsin-Madison, just received another $21 million in funding for phase 2, the company said.

Computer scientists have been working for some time on systems that can emulate the brain’s massively parallel, low-power computing prowess, and they’ve made several breakthroughs. Last year, computer engineer Steve Furber described a synaptic computer network that consists of tens of thousands of cellphone chips.

The most notable computer-brain achievements have been in the field of memristors. As their name implies, a memory resistor can “remember” the last resistance that it possessed when current was flowing through it — so after current is turned back on, the resistance of the circuit will be the same. We will not attempt to delve too deeply here, but this basically makes a system much more efficient.