Wafer‐Scale 2D Hafnium Diselenide Based Memristor Crossbar Array for Energy‐Efficient Neural Network Hardware

2021 ◽  
pp. 2103376 ◽  
Author(s):  
Sifan Li ◽  
Mei‐Er Pam ◽  
Yesheng Li ◽  
Li Chen ◽  
Yu‐Chieh Chien ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Wafer Scale ◽  
Crossbar Array ◽  
Neural Network Hardware ◽  
Memristor Crossbar

Nano-Crossbar Weighted Memristor-Based Convolution Neural Network Architecture for High-Performance Artificial Intelligence Applications

2021 ◽  
Vol 21 (3) ◽  
pp. 1833-1844
Author(s):  
Kyojin Kim ◽  
Kamran Eshraghian ◽  
Hyunsoo Kang ◽  
Kyoungrok Cho
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
High Performance ◽  
Detection Algorithm ◽  
Cross Point ◽  
Crossbar Array ◽  
Array Architecture ◽  
Keyword Detection ◽  
Memristor Crossbar

Nano memristor crossbar arrays, which can represent analog signals with smaller silicon areas, are popularly used to describe the node weights of the neural networks. The crossbar arrays provide high computational efficiency, as they can perform additions and multiplications at the same time at a cross-point. In this study, we propose a new approach for the memristor crossbar array architecture consisting of multi-weight nano memristors on each cross-point. As the proposed architecture can represent multiple integer-valued weights, it can enhance the precision of the weight coefficients in comparison with the existing memristor-based neural networks. This study presents a Radix-11 nano memristor crossbar array with weighted memristors; it validates the operations of the circuits, which use the arrays through circuit-level simulation. With the proposed Radix-11 approach, it is possible to represent eleven integer-valued weights. In addition, this study presents a neural network designed using the proposed Radix-11 weights, as an example of high-performance AI applications. The neural network implements a speech-keyword detection algorithm, and it was designed on a TensorFlow platform. The implemented keyword detection algorithm can recognize 35 Korean words with an inferencing accuracy of 95.45%, reducing the inferencing accuracy only by 2% when compared to the 97.53% accuracy of the real-valued weight case.

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