Publication | Closed Access
A self-learning neural network composed of 1152 digital neurons in wafer-scale LSIs
27
Citations
2
References
1991
Year
Unknown Venue
EngineeringMachine LearningNeural Networks (Machine Learning)Computer ArchitectureDigital NeuronsWafer-scale LsisNeurochipSocial SciencesComputing SystemsEmbedded Machine LearningNeuromorphic EngineeringNeurocomputersSignature VerificationComputer EngineeringNeural Networks (Computational Neuroscience)Computer ScienceDeep LearningNeural Architecture SearchDeep Neural NetworksComputational NeuroscienceSelf-learning Neural NetworkBack PropagationBp SimulationNeuronal NetworkBrain-like ComputingTechnologyClassical Machine Learning
The design, fabrication, and evaluation of a compact self-learning neural network made up of more than 1000 neurons are described. A time-sharing bus architecture decreases the number of circuits required and makes possible flexible and expandable networks. Neural functions and the back propagation (BP) algorithm were mapped to binary digital circuits. A dual-network architecture allows high-speed learning. This hardware can be connected to a host workstation and used for a wide range of artificial neural networks. Signature verification and stock price prediction have already been demonstrated with this hardware. The peak learning speed was about 10 times faster than BP simulation by an S-820 Hitachi supercomputer.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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