Brain Power


A brain-inspired chip to transform mobility and Internet of Things through sensory perception

Transforming mobile
Jellyfish sensors
Sensor leaves
Autonomous robot
Smart thermometer


Infographic: Brain Power!

What is a cognitive chip? The latest SyNAPSE chip, introduced on August 7, 2014, has the potential to transform mobility by spurring innovation around an entirely new class of applications with sensory capabilities at incredibly low power levels. This is enabled by an revolutionary new technology design inspired by the human brain. IBM built a new chip with a brain-inspired computer architecture powered by an unprecedented 1 million neurons and 256 million synapses. It is the largest chip IBM has ever built at 5.4 billion transistors, and has an on-chip network of 4,096 neurosynaptic cores. Yet, it only consumes 70mW during real-time operation — orders of magnitude less energy than traditional chips. As part of a complete cognitive hardware and software ecosystem, this technology opens new computing frontiers for distributed sensor and supercomputing applications.

Epic Shift to Low-Power Supercomputers

Dharmendra Modha

Dharmendra Modha, IBM Fellow

The architecture can solve a wide class of problems from vision, audition, and multi-sensory fusion, and has the potential to revolutionize the computer industry by integrating brain-like capability into devices where computation is constrained by power and speed.

— Dharmendra Modha, IBM Fellow

Read Dr. Modha's SyNAPSE article, here.


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Neurosynaptic systems

Building applications for cognitive computers

IBM’s brain-inspired architecture consists of a network of neurosynaptic cores. Cores are distributed and operate in parallel. Cores operate—without a clock—in an event-driven fashion. Cores integrate memory, computation, and communication. Individual cores can fail and yet, like the brain, the architecture can still function. Cores on the same chip communicate with one another via an on-chip event-driven network. Chips communicate via an inter-chip interface leading to seamless scalability like the cortex, enabling creation of scalable neuromorphic systems.

A video camera on Hoover Tower at Stanford University is looking down at the plaza, below. A simulated network of IBM TrueNorth chips takes in the video data and locates interesting objects. Objects might look interesting to the system because they are moving or have a different color or texture than the background. The system then further processes those portions of the interesting video to determine what the objects are. It is trained in several specific categories, such as buses, cars, people, and cyclists. In a monitoring application, the camera would only need to communicate when it found an interesting object, rather than continually streaming video to a central location.


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Synapse University

Programming this new architecture requires a fundamentally new way of thinking, so IBM Research built SyNAPSE University — a curriculum of lectures, hands-on exercises, and expert coaching that helps interested parties build these complex neurosynaptic systems. It will be a place to work with current, and develop new, business and academic partnerships. If you are interested in collaborating with IBM Research, please fill out our online information form.


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