The brain-like, neural network design of the IBM Neuromorphic System is able to infer complex cognitive tasks such as pattern recognition and integrated sensory processing more efficiently than conventional chips.
The system will be used to explore computing capabilities important to the National Nuclear Security Administration’s (NNSA) missions in cyber security, stewardship of the US nuclear deterrent and non-proliferation. NNSA’s Advanced Simulation and Computing (ASC) program will evaluate machine learning applications, deep learning algorithms and architectures and conduct general computing feasibility studies.
“Neuromorphic computing opens very exciting new possibilities and is consistent with what we see as the future of the high performance computing and simulation at the heart of our national security missions,” said Jim Brase, LLNL deputy associate director for Data Science. “The potential capabilities neuromorphic computing represents and the machine intelligence that these will enable will change how we do science.”
The technology represents a fundamental departure from computer design that has been prevalent for the past 70 years and could be a powerful complement in the development of next-generation supercomputers able to perform at exascale speeds, 50 times faster than today’s most advanced petaflop systems.
Michel McCoy, LLNL programme director for Weapon Simulation and Computing, said: “The low power consumption of these brain-inspired processors reflects the industry’s desire and a creative approach to reducing power consumption in all components for future systems as we set our sights on exascale computing.”
“The delivery of this advanced computing platform represents a major milestone as we enter the next era of cognitive computing,” said Dharmendra Modha, chief scientist, brain-inspired computing, IBMResearch. “This collaboration will push the boundaries of brain-inspired computing to enable future systems that deliver unprecedented capability and throughput, while helping to minimise the capital, operating and programming costs.”
