Browsing by Author "He, Yu-Hui"

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High-Precision Symmetric Weight Update of Memristor by Gate Voltage Ramping Method for Convolutional Neural Network Accelerator
(2020-03-01)
Chen, Jia
;
Pan, Wen-Qian
;
Li, Yi
;
Kuang, Rui
;
He, Yu-Hui
Memristor emerges as the key enabler for neural network accelerator. Here, we demonstrate high-precision symmetric weight update in a one transistor one resistor (1T1R) structure Ti/HfO2/TiN memristor using a gate voltage ramping method, with over 120-level states and low variation (< 4%). Incorporating all experimental non-idealities, the proposed mixed hardware-software convolutional neural network demonstrates over 92.79% online learning accuracy (against software equivalent 98.45%) for MNIST recognition task. The network also shows robustness to input image noises, array yield, and retention issues.
LiSiOX-Based Analog Memristive Synapse for Neuromorphic Computing
(2019-04-01)
Chen, Jia
;
Lin, Chih-Yang
;
Li, Yi
;
Qin, Chao
;
Lu, Ke
The progress in the neuromorphic computing hinges on the development of nanoscale analog artificial synapses. Here, we report a LiSiOX (LSO)-based memristive synapse with 100-level conductance states under identical pulses, representing synaptic potentiation and depression behaviors. The superior analog behaviors originate from the dynamic evolution of an electro-thermal modulation region with the motion of lithium and oxygen ions. A three-layer perceptron was constructed in simulation with LSO synapses, and a 91.97% recognition accuracy was achieved for handwritten digits. Moreover, the influences of several critical parameters, including device variability and weight precision, on the accuracy have been investigated. This letter provides guidelines for the optimization of synaptic device in robust memristive neural network.