Lawrence Livermore National Laboratory has purchased IBM’s platform for deep-learning, based on 16TrueNorth neurosynaptic chips, to explore deep learning algorithms. IBM says the scalable platform processing power is the equivalent of 16 million artificial “neurons” and 4 billion “synapses.”
The brain-like neural-network design of the IBM Neuromorphic System can process complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips, says IBM.
The technology represents a fundamental departure from computer design that has been prevalent for the past 70 years and could be incorporated in next-generation supercomputers able to perform at exascale speeds, 50 times faster than today’s most advanced petaflop (quadrillion floating point operations per second) systems.
The TrueNorth processor chip was introduced in 2014 (see IBM launches functioning brain-inspired chip). It consists of 5.4 billion transistors wired together to create an array of 1 million digital “neurons” that communicate with one another via 256 million electrical “synapses.”
Like the human brain, neurosynaptic systems require significantly less electrical power and volume. The 16 TrueNorth chips consume the energy equivalent of only a tablet computer, 2.5 watts of power. At 0.8 volts, each chip consumes 70 milliwatts of power running in real time and delivers 46 giga synaptic operations per second.
TrueNorth was originally developed under the auspices of DARPA’s Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program in collaboration with Cornell University (see IBM simulates 530 billion neurons, 100 trillion synapses on supercomputer).
“The delivery of this advanced computing platform represents a major milestone as we enter the next era of cognitive computing,” said Dharmendra S. Modha, IBM Fellow, chief scientist, brain-inspired computing, IBM Research, Almaden. “Prior to design and fabrication, we simulated the IBM TrueNorth processor using LLNL’s Sequoia supercomputer.
This collaboration will push the boundaries of brain-inspired computing to enable future systems that deliver unprecedented capability and throughput, while helping to minimize the capital, operating, and programming costs.”