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IBM Research has created a new programming model to support its chips that mimic the workings of the human brain, known as Systems of Neuromorphic Adaptive Plastic Scalable Electronics, or SyNAPSE, chips.
In an effort in which a portion of the project was funded by the Defense Advanced Research Projects Agency (DARPA), IBM on Aug. 8 announced a breakthrough software ecosystem designed for programming silicon chips that have an architecture inspired by the function, low power and compact volume of the brain. The technology could enable a new generation of intelligent sensor networks that mimic the brain’s abilities for perception, action and cognition, IBM said.
IBM’s long-term goal is to build a chip system with 10 billion neurons and a hundred trillion synapses, while consuming merely 1kilowatt of power and occupying less than two liters of volume, Big Blue said.
To get there, IBM researchers knew they had to deliver not only the new hardware, but a new software paradigm. IBM said the new programming model is dramatically different from traditional software. Indeed, 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.
Cognitive computing, of course, is nothing new to IBM. Its Watson supercomputer is perhaps the best example of cognitive computing the company has to offer. “Cognitive computing systems are not based on programs that predetermine every answer or action needed to perform a function or set of tasks; rather, they are trained using artificial intelligence (AI) and machine learning algorithms to sense, predict, infer and, in some ways, think,” IBM says on its Web page defining cognitive computing.
“Architectures and programs are closely intertwined, and a new architecture necessitates a new programming paradigm,” said Dr. Dharmendra S. Modha, principal investigator and senior manager for the project in IBM Research. “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.”
To advance and enable this new ecosystem, IBM researchers developed the following breakthroughs that support all aspects of the programming cycle from design through development, debugging and deployment:
- Simulator: A multithreaded, 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 brainlike 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, multimodal 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, multimodal, spatio-temporal sensors and actuators together in real time. In less than a year, the IBM researchers have designed and stored more than 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.
IBM Research Creates Programming Model to Mimic Human Brain Power
IBM is presenting these innovations this week at The International Joint Conference on Neural Networks in Dallas, Texas.
IBM notes that modern computing systems were designed decades ago for sequential processing according to a predefined program. Although they are fast and precise “number crunchers,” computers of traditional design become constrained by power and size while operating at reduced effectiveness when applied to real-time processing of the noisy, analog, voluminous, big data. In contrast, the brain—which operates comparatively slowly and at low precision—excels at tasks such as recognizing, interpreting and acting upon patterns, while consuming the same amount of power as a 20-watt light bulb and occupying the volume of a two-liter bottle, IBM said.
In other words, as IBM’s Modha told Forbes, “Think of today’s computers as left brained and SyNAPSE as right brained.” Being left-brained refers to being more analytical, logical and objective, thus SyNAPSE is more intuitive, thoughtful and subjective, as these are right-brained characteristics.
In August 2011, IBM demonstrated a building block of a novel brain-inspired chip architecture based on a scalable, interconnected, configurable network of “neurosynaptic cores.” Each core brings memory (“synapses”), processors (“neurons”) and communication (“axons”) in close proximity, executing activity in an event-driven fashion. These chips serve as a platform for emulating and extending the brain’s ability to respond to biological sensors and analyzing vast amounts of data from many sources at once, IBM said.
Having completed Phase 0, Phase 1 and Phase 2, IBM and its collaborators (Cornell University and iniLabs) have recently been awarded $12 million in new funding from DARPA for Phase 3 of the SyNAPSE project, thus bringing the cumulative funding to $53 million.
IBM said systems built from these chips could bring the real-time capture and analysis of various types of data closer to the point of collection. They would not only gather symbolic data, which is fixed text or digital information, but also gather sub-symbolic data, which is sensory based and whose values change continuously. This raw data reflects activity in the world of every kind, ranging from commerce, social, logistics, location, movement and environmental conditions.
For example, IBM said, the human eyes sift through over a terabyte of data per day. Emulating the visual cortex, low-power, lightweight eye glasses designed to help the visually impaired could be outfitted with multiple video and auditory sensors that capture and analyze this optical flow of data.
These sensors would gather and interpret large-scale volumes of data to signal how many individuals are ahead of the user, the distance to an upcoming curb, the number of vehicles in a given intersection and the height of a ceiling or length of a crosswalk. Like a guide dog, sub-symbolic data perceived by the glasses would allow users to plot the safest pathway through a room or outdoor setting and help him or her navigate the environment via embedded speakers or ear buds.
This same technology—at increasing levels of scale—can form sensory-based data input capabilities and on-board analytics for automobiles, medical imagers, health care devices, smartphones, cameras and robots, IBM said.