Via PCMag
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IBM on Thursday announced a new computer programming framework that
draws inspiration from the way the human brain receives data, processes
it, and instructs the body to act upon it while requiring relatively
tiny amounts of energy to do so.
"Dramatically different from traditional software, IBM's new programming
model breaks the mold of sequential operation underlying today's von
Neumann architectures and computers. It is instead tailored for a new
class of distributed, highly interconnected, asynchronous, parallel,
large-scale cognitive computing architectures," IBM said in a statement
introducing recent advances made by its Systems of Neuromorphic Adaptive
Plastic Scalable Electronics (SyNAPSE) project.
IBM and research partners Cornell University and iniLabs have completed
the second phase of the approximately $53 million project. With $12
million in new funding from the Defense Advanced Research Projects
Agency (DARPA), IBM said work is set to commence on Phase 3, which will
involve an ambitious plan to develop intelligent sensor networks built
on a "brain-inspired chip architecture" using a "scalable,
interconnected, configurable network of 'neurosynaptic cores'."
"Architectures and programs are closely intertwined and a new
architecture necessitates a new programming paradigm," Dr. Dharmendra
Modha, principal investigator and senior manager, IBM Research, said in a statement.
"We are working to create a FORTRAN for synaptic computing chips. While
complementing today's computers, this will bring forth a fundamentally
new technological capability in terms of programming and applying
emerging learning systems."
Going forward, work on the project will focus on honing a programming
language for the SyNAPSE chip architecture first shown by IBM in 2011,
with an agenda of using the new framework to deal with "big data"
problems more efficiently.
IBM listed the following tools and systems it has developed with its partners towards this end:
- Simulator: A multi-threaded, massively parallel and highly
scalable functional software simulator of a cognitive computing
architecture comprising a network of neurosynaptic cores.
- Neuron Model: A simple, digital, highly parameterized spiking
neuron model that forms a fundamental information processing unit of
brain-like computation and supports a wide range of deterministic and
stochastic neural computations, codes, and behaviors. A network of such
neurons can sense, remember, and act upon a variety of spatio-temporal,
multi-modal environmental stimuli.
- Programming Model: A high-level description of a "program"
that is based on composable, reusable building blocks called "corelets."
Each corelet represents a complete blueprint of a network of
neurosynaptic cores that specifies a based-level function. Inner
workings of a corelet are hidden so that only its external inputs and
outputs are exposed to other programmers, who can concentrate on what
the corelet does rather than how it does it. Corelets can be combined to
produce new corelets that are larger, more complex, or have added
functionality.
- Library: A cognitive system store containing designs and
implementations of consistent, parameterized, large-scale algorithms and
applications that link massively parallel, multi-modal, spatio-temporal
sensors and actuators together in real-time. In less than a year, the
IBM researchers have designed and stored over 150 corelets in the
program library.
- Laboratory: A novel teaching curriculum that spans the
architecture, neuron specification, chip simulator, programming
language, application library and prototype design models. It also
includes an end-to-end software environment that can be used to create
corelets, access the library, experiment with a variety of programs on
the simulator, connect the simulator inputs/outputs to
sensors/actuators, build systems, and visualize/debug the results.
