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Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches
| Content Provider | MDPI |
|---|---|
| Author | Eduardo, Pérez Francisco, Jiménez-Molinos Juan, Roldán Romero-Zaliz, Rocío Wenger, Christian |
| Copyright Year | 2021 |
| Description | A comprehensive analysis of two types of artificial neural networks (ANN) is performed to assess the influence of quantization on the synaptic weights. Conventional multilayer-perceptron (MLP) and convolutional neural networks (CNN) have been considered by changing their features in the training and inference contexts, such as number of levels in the quantization process, the number of hidden layers on the network topology, the number of neurons per hidden layer, the image databases, the number of convolutional layers, etc. A reference technology based on 1T1R structures with bipolar memristors including |
| Starting Page | 346 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics10030346 |
| Journal | Electronics |
| Issue Number | 3 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-02-01 |
| Access Restriction | Open |
| Subject Keyword | Electronics Hardware and Architecturee Memristor Multilevel Operation Hardware Neural Network Deep Neural Network Convolutional Neural Network Image Recognition |
| Content Type | Text |
| Resource Type | Article |
