Scalable Neuron Circuit Using Conductive-Bridge RAM for Pattern Reconstructions

— A novel neuron circuit using a Cu/Ti/Al2O3-based conductive-bridge random access memory (CBRAM) device for hardware neural networks that utilize nonvolatile memories as synaptic weights is introduced. The neuronal operations are designed and proved using SPICE simulations with a Verilog-A device model based on the measured characteristics of the CBRAM device. The applicability of the neuron is demonstrated by constructing a neural network system and applying it to pattern reconstructions that can recall the original patterns from noisy patterns. With these CBRAM-based neurons, a reduction in the area and power of neuromorphic chips is expected in comparison with CMOS-only neuron implementations.


Published in:
IEEE Transactions on Electron Devices
Year:
2016
Publisher:
Piscataway, Institute of Electrical and Electronics Engineers
ISSN:
0018-9383
Keywords:
Laboratories:




 Record created 2016-05-05, last modified 2018-09-13


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