Intrinsic variation effect in memristive neural network with weight quantization.

Autor: Park J; Department of Electronic Engineering, Inha University, Incheon 22212, Republic of Korea., Song MS; Department of Electronic Engineering, Inha University, Incheon 22212, Republic of Korea., Youn S; Department of Electronic Engineering, Inha University, Incheon 22212, Republic of Korea., Kim TH; Department of Electrical and Computer Engineering, Seoul National University, Seoul 151742, Republic of Korea., Kim S; Department of Electrical and Computer Engineering, Seoul National University, Seoul 151742, Republic of Korea., Hong K; Department of Electrical and Computer Engineering, Seoul National University, Seoul 151742, Republic of Korea., Kim H; Department of Electronic Engineering, Inha University, Incheon 22212, Republic of Korea.
Jazyk: angličtina
Zdroj: Nanotechnology [Nanotechnology] 2022 Jun 24; Vol. 33 (37). Date of Electronic Publication: 2022 Jun 24.
DOI: 10.1088/1361-6528/ac7651
Abstrakt: To analyze the effect of the intrinsic variations of the memristor device on the neuromorphic system, we fabricated 32 × 32 Al 2 O 3 /TiO x -based memristor crossbar array and implemented 3 bit multilevel conductance as weight quantization by utilizing the switching characteristics to minimize the performance degradation of the neural network. The tuning operation for 8 weight levels was confirmed with a tolerance of ±4 μ A (±40 μ S). The endurance and retention characteristics were also verified, and the random telegraph noise (RTN) characteristics were measured according to the weight range to evaluate the internal stochastic variation effect. Subsequently, a memristive neural network was constructed by off-chip training with differential memristor pairs for the Modified National Institute of Standards and Technology (MNIST) handwritten dataset. The pre-trained weights were quantized, and the classification accuracy was evaluated by applying the intrinsic variations to each quantized weight. The intrinsic variations were applied using the measured weight inaccuracy given by the tuning tolerance, RTN characteristics, and the fault device yield. We believe these results should be considered when the pre-trained weights are transferred to a memristive neural network by off-chip training.
(© 2022 IOP Publishing Ltd.)
Databáze: MEDLINE
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