LLNL Partners with IBM on Brain-Like Computing Chip
Abstract
Lawrence Livermore National Laboratory (LLNL) will receive a first-of-a-kind brain-inspired supercomputing platform for deep learning developed by IBM Research. Based on a breakthrough neurosynaptic computer chip called IBM TrueNorth, the scalable platform will process the equivalent of 16 million neurons and 4 billion synapses and consume the energy equivalent of a hearing aid battery – a mere 2.5 watts of power. 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 far more efficiently than conventional chips.
- Authors:
- Publication Date:
- Research Org.:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1245171
- Resource Type:
- Multimedia
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; IBM; TRUENORTH; IBM TRUENORTH NEUROMORPHIC CHIP; HUMAN BRAIN; TRUENORTH ARCHITECTURE; COMPUTING ARCHITECTURE; ADVANCED SIMULATION
Citation Formats
Van Essen, Brian. LLNL Partners with IBM on Brain-Like Computing Chip. United States: N. p., 2016.
Web.
Van Essen, Brian. LLNL Partners with IBM on Brain-Like Computing Chip. United States.
Van Essen, Brian. Tue .
"LLNL Partners with IBM on Brain-Like Computing Chip". United States. https://www.osti.gov/servlets/purl/1245171.
@article{osti_1245171,
title = {LLNL Partners with IBM on Brain-Like Computing Chip},
author = {Van Essen, Brian},
abstractNote = {Lawrence Livermore National Laboratory (LLNL) will receive a first-of-a-kind brain-inspired supercomputing platform for deep learning developed by IBM Research. Based on a breakthrough neurosynaptic computer chip called IBM TrueNorth, the scalable platform will process the equivalent of 16 million neurons and 4 billion synapses and consume the energy equivalent of a hearing aid battery – a mere 2.5 watts of power. 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 far more efficiently than conventional chips.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {3}
}