Today, IBM
researchers unveiled a new generation of experimental computer chips
designed to emulate the brain’s abilities for perception, action and
cognition. The technology could yield many orders of magnitude less
power consumption and space than used in today’s computers.
In
a sharp departure from traditional 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.
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.
To
do this, IBM is combining principles from nanoscience, neuroscience and
supercomputing as part of a multi-year cognitive computing initiative.
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. The IBM
team has already successfully completed Phases 0 and 1.
“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
While
they contain no biological elements, IBM’s first cognitive computing
prototype chips use digital silicon circuits inspired by neurobiology to
make up what is referred to as 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
SOI-CMOS 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
light-weight cores, creating a single integrated system of hardware and
software. This architecture represents a critical shift away from
traditional von Neumann computing to 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.
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.
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 Modha.
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.
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. Watson represents
a tremendous breakthrough in computers understanding natural language,
“real language” that is not specially designed or encoded just for
computers, but language that humans use to naturally capture and
communicate knowledge.
IBM’s
cognitive computing chips were built at its highly advanced chip-making
facility in Fishkill, N.Y. and are currently being tested at its
research labs in Yorktown Heights, N.Y. and San Jose, Calif.
