ConvNets Architecture for Complex Mixed Analogue-Digital Simulations

被引：0

作者：

Bonaiuto, Vincenzo ^{[1
]}

Sargeni, Fausto ^{[2
]}

机构：

[1] Univ Roma Tor Vergata, Dept Ind Engn, Rome, Italy

[2] Univ Roma Tor Vergata, Dept Elect Engn, Rome, Italy

来源：

2020 32ND INTERNATIONAL CONFERENCE ON MICROELECTRONICS (ICM) | 2020年

关键词：

Analog Circuit; Current Mode Circuits; Convolutional Neural Networks; Cellular Neural Network; NEURAL-NETWORKS;

D O I：

10.1109/ICM50269.2020.9331822

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Convolutional Neural Networks (ConvNets) with its proper hierarchical structure are known as powerful image-recognition processing architecture. In particular, the ConvNets are well suited for several image processing tasks, such as image classification data set, computer vision and natural language processing. Nevertheless, the implementation of ConvNets requires a large amount of operations as the 2-D convolutional mappings that need a very large computational power. In this paper, the authors will investigate alternative hardware architectures, based on Cellular Neural Networks (CeNNs), in order to improve the overall performances

引用

页码：91 / 94

页数：4

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