Publication | Closed Access
Inherent Stochastic Learning in CMOS-Integrated HfO<sub>2</sub> Arrays for Neuromorphic Computing
28
Citations
15
References
2019
Year
New Learning AlgorithmMachine LearningNeural Networks (Machine Learning)EngineeringStochastic Learning AlgorithmSocial SciencesUnconventional ComputingMemory DevicesNeuromorphic EngineeringNeuromorphic DevicesNeurocomputersElectrical EngineeringRram TechnologiesComputer EngineeringNeuromorphic ComputingComputer ScienceNeural Networks (Computational Neuroscience)Neural InterfaceBrain-computer InterfaceNeuroengineeringComputational NeuroscienceInherent Stochastic LearningNeuroscienceBrain-like ComputingResistive Random-access Memory
Based on the inherent stochasticity of CMOS-integrated HfO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -based resistive random access memory (RRAM) devices, a new learning algorithm for neuro-morphic systems is presented. For this purpose, the device-to-device variability of CMOS-integrated 4-kbit 1T-1R arrays is examined. To demonstrate the performance of the stochastic learning algorithm and the potential of RRAM technologies for neuro-morphic systems, a two-layer mixed-signal neural circuit for pattern recognition is implemented and tested with MNIST data.
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