IBM (News - Alert) Research, in collaboration with DARPA's Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program, has reached another brain simulation milestone. IBM researchers were capable of crafting 2.084 billion neurosynaptic cores containing 530 billion neurons and 100 trillion synapses. All at a speed "only" 1542 times slower than real-time, wrote Engadget reporter Mat Smith.

To accomplish this milestone, IBM utilized its new TrueNorth system powered by the world’s second fastest supercomputer, the Lawrence Livermore National Lab (LBNL) Blue Gene/Q Sequoia, using 96 racks (1,572,864 processor cores, 1.5 PB memory, 98,304 MPI processes, and 6,291,456 threads).

As per this report, DARPA's SyNAPSE project aims to tie together supercomputing, neuroscience and neurotech for a future cognitive computing architecture. The results were presented at the Supercomputing 2012.

DARPA’s SyNAPSE program was unveiled in 2008 for developing electronic neuromorphic (brain-simulation) machine technology that scales to biological levels, using a cognitive computing architecture with 1010 neurons (10 billion) and 1014 synapses (100 trillion), based on estimates of the number of synapses in the human brain to develop electronic neuromorphic machine technology that scales to biological levels.  

“We have not built a biologically realistic simulation of the complete human brain,” explains an abstract of the IBM Supercomputing 2012 paper. While neurosynaptic core and its applications have been demonstrated earlier, the current paper abstract shows that the researchers have compiled the largest long-distance wiring diagram of the monkey brain. It demonstrates a network with over 2 billion neurosynaptic cores that are divided into 77 brain-inspired regions with probabilistic intra-region (“gray matter”) connectivity and monkey-brain-inspired inter-region (“white matter”) connectivity.

According to IBM’s abstract, “This fulfills a core vision of the DARPA SyNAPSE project to bring together nanotechnology, neuroscience, and supercomputing to lay the foundation of a novel cognitive computing architecture that complements today’s von Neumann machines.”

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