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A Low Energy Oxide‐Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation
Author(s) -
Yu Shimeng,
Gao Bin,
Fang Zheng,
Yu Hongyu,
Kang Jinfeng,
Wong H.S. Philip
Publication year - 2013
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201203680
Subject(s) - neuromorphic engineering , von neumann architecture , computer science , resistive random access memory , enhanced data rates for gsm evolution , energy consumption , energy (signal processing) , key (lock) , materials science , artificial intelligence , artificial neural network , electrical engineering , voltage , engineering , operating system , statistics , mathematics , computer security
Neuromorphic computing is an emerging computing paradigm beyond the conventional digital von Neumann computation. An oxide‐based resistive switching memory is engineered to emulate synaptic devices. At the device level, the gradual resistance modulation is characterized by hundreds of identical pulses, achieving a low energy consumption of less than 1 pJ per spike. Furthermore, a stochastic compact model is developed to quantify the device switching dynamics and variation. At system level, the performance of an artificial visual system on the image orientation or edge detection with 16 348 oxide‐based synaptic devices is simulated, successfully demonstrating a key feature of neuromorphic computing: tolerance to device variation.