Via Kurzweil
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IBM researchers unveiled today a new generation of experimental
computer chips designed to emulate the brain’s abilities for perception,
action and cognition.
In a sharp departure from traditional von
Neumann computing concepts in designing and building computers, IBM’s
first neurosynaptic computing chips recreate the phenomena between
spiking neurons and synapses in biological systems, such as the brain,
through advanced algorithms and silicon circuitry.
The technology
could yield many orders of magnitude less power consumption and space
than used in today’s computers, the researchers say. Its first two
prototype chips have already been fabricated and are currently
undergoing testing.
Called cognitive computers,
systems built with these chips won’t be programmed the same way
traditional computers are today. Rather, cognitive computers are
expected to learn through experiences, find correlations, create
hypotheses, and remember — and learn from — the outcomes, mimicking the
brains structural and synaptic plasticity.
“This is a major
initiative to move beyond the von Neumann paradigm that has been ruling
computer architecture for more than half a century,” said Dharmendra
Modha, project leader for IBM Research.
“Future applications of
computing will increasingly demand functionality that is not efficiently
delivered by the traditional architecture. These chips are another
significant step in the evolution of computers from calculators to
learning systems, signaling the beginning of a new generation of
computers and their applications in business, science and government.”
Neurosynaptic chips
IBM
is combining principles from nanoscience, neuroscience, and
supercomputing as part of a multi-year cognitive computing initiative.
IBM’s long-term goal is to build a chip system with ten billion neurons
and hundred trillion synapses, while consuming merely one kilowatt of
power and occupying less than two liters of volume.
While
they contain no biological elements, IBM’s first cognitive computing
prototype chips use digital silicon circuits inspired by neurobiology
to make up a “neurosynaptic core” with integrated memory (replicated
synapses), computation (replicated neurons) and communication
(replicated axons).
IBM has two working prototype designs. Both
cores were fabricated in 45 nm SOICMOS and contain 256 neurons. One
core contains 262,144 programmable synapses and the other contains
65,536 learning synapses. The IBM team has successfully demonstrated
simple applications like navigation, machine vision, pattern
recognition, associative memory and classification.
IBM’s
overarching cognitive computing architecture is an on-chip network of
lightweight cores, creating a single integrated system of hardware and
software. It represents a potentially more power-efficient architecture
that has no set programming, integrates memory with processor, and
mimics the brain’s event-driven, distributed and parallel processing.
Visualization of the long distance network of a monkey brain (credit: IBM Research)
SyNAPSE
The
company and its university collaborators also announced they have been
awarded approximately $21 million in new funding from the Defense
Advanced Research Projects Agency (DARPA) for Phase 2 of the Systems of
Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project.
The
goal of SyNAPSE is to create a system that not only analyzes complex
information from multiple sensory modalities at once, but also
dynamically rewires itself as it interacts with its environment — all
while rivaling the brain’s compact size and low power usage.
For
Phase 2 of SyNAPSE, IBM has assembled a world-class multi-dimensional
team of researchers and collaborators to achieve these ambitious goals.
The team includes Columbia University; Cornell University; University
of California, Merced; and University of Wisconsin, Madison.
Why Cognitive Computing
Future
chips will be able to ingest information from complex, real-world
environments through multiple sensory modes and act through multiple
motor modes in a coordinated, context-dependent manner.
For
example, a cognitive computing system monitoring the world’s water
supply could contain a network of sensors and actuators that constantly
record and report metrics such as temperature, pressure, wave height,
acoustics and ocean tide, and issue tsunami warnings based on its
decision making.
Similarly, a grocer stocking shelves could use an
instrumented glove that monitors sights, smells, texture and
temperature to flag bad or contaminated produce. Making sense of
real-time input flowing at an ever-dizzying rate would be a Herculean
task for today’s computers, but would be natural for a brain-inspired
system.
“Imagine traffic lights that can integrate sights, sounds
and smells and flag unsafe intersections before disaster happens or
imagine cognitive co-processors that turn servers, laptops, tablets, and
phones into machines that can interact better with their environments,”
said Dr. Modha.
IBM has a rich history in the area of artificial
intelligence research going all the way back to 1956 when IBM performed
the world’s first large-scale (512 neuron) cortical simulation. Most
recently, IBM Research scientists created Watson,
an analytical computing system that specializes in understanding
natural human language and provides specific answers to complex
questions at rapid speeds.
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IBM research