Version 1
: Received: 30 January 2019 / Approved: 31 January 2019 / Online: 31 January 2019 (06:54:33 CET)
How to cite:
Sun, S.; Xu, H.; Li, J.; Sun, Y.; Li, Q.; Li, Z.; Liu, H. A Memristor-Based Cascaded Neural Networks for Specific Target Recognition. Preprints2019, 2019010319. https://doi.org/10.20944/preprints201901.0319.v1.
Sun, S.; Xu, H.; Li, J.; Sun, Y.; Li, Q.; Li, Z.; Liu, H. A Memristor-Based Cascaded Neural Networks for Specific Target Recognition. Preprints 2019, 2019010319. https://doi.org/10.20944/preprints201901.0319.v1.
Cite as:
Sun, S.; Xu, H.; Li, J.; Sun, Y.; Li, Q.; Li, Z.; Liu, H. A Memristor-Based Cascaded Neural Networks for Specific Target Recognition. Preprints2019, 2019010319. https://doi.org/10.20944/preprints201901.0319.v1.
Sun, S.; Xu, H.; Li, J.; Sun, Y.; Li, Q.; Li, Z.; Liu, H. A Memristor-Based Cascaded Neural Networks for Specific Target Recognition. Preprints 2019, 2019010319. https://doi.org/10.20944/preprints201901.0319.v1.
Abstract
Multiply-accumulate calculations using a memristor crossbar array is an important method to realize neuromorphic computing. However, the memristor array fabrication technology is still immature, and it is difficult to fabricate large-scale arrays with high-yield, which restricts the development of memristor-based neuromorphic computing technology. Therefore, cascading small-scale arrays to achieve the neuromorphic computational ability that can be achieved by large-scale arrays, which is of great significance for promoting the application of memristor-based neuromorphic computing. To address this issue, we present a memristor-based cascaded framework with some basic computation units, several neural network processing units can be cascaded by this means to improve the processing capability of the dataset. Besides, we introduce a split method to reduce pressure of input terminal. Compared with VGGNet and GoogLeNet, the proposed cascaded framework can achieve 93.54% Fashion-MNIST accuracy under the 4.15M parameters. Extensive experiments with Ti/AlOx/TaOx/Pt we fabricated are conducted to show that the circuit simulation results can still provide a high recognition accuracy, and the recognition accuracy loss after circuit simulation can be controlled at around 0.26%.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
