The goal of SyNAPSE is to develop biological-scale neuromorphic electronic systems that mimic key functions of a human brain.

DARPA Developing Brain-Modeled Machines

A "new generation of intelligent electronic machines" would have applications in robotics and manned systems and in sensory and integration applications such as image processing, it says.

The Defense Advanced Research Projects Agency is making progress toward what it describes as a "new generation of intelligent electronic machines" through work by SyNAPSE, DARPA's Systems of Neuromorphic Adaptive Plastic Scalable Electronics program. DARPA posted a news release about the work on April 7.

The goal of SyNAPSE is to develop biological-scale neuromorphic electronic systems that mimic key functions of a human brain. This breakthrough would be a new kind of computer with applications in robotics and manned systems and in sensory and integration applications such as image processing, according to the agency's release.

"Using knowledge of the brain's organization as a platform, SyNAPSE is developing integrated circuits with high densities of electronic devices and integrated communication networks that approximate the function and connectivity of neurons and synapses. This program has also developed tools to support this specific area of hardware development such as circuit design tools, large-scale computer simulations of hardware function, and virtual training environments that can test and benchmark these systems," it says.

"So far, SyNAPSE has successfully demonstrated all the core hardware, architecture, simulation, and evaluation capabilities needed for a new generation of intelligent electronic machines," said Todd Hylton, SyNAPSE program manager. The next phase of work will include chip-fabrication process development, design and validation of single-chip systems, and demonstration of these systems in virtual environments. "Now that all the building blocks are available, our next task is to start building functioning systems out of them," Hylton said.

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