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.
Here's Modha explaining the goals and breakthroughs of the SyNAPSE further: