| Autor: |
Daniel J. Mannion, Adnan Mehonic, Wing H. Ng, Anthony J. Kenyon |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Frontiers in Neuroscience, Vol 13 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
1662-453X |
| DOI: |
10.3389/fnins.2019.01386 |
| Popis: |
Memristors have many uses in machine learning and neuromorphic hardware. From memory elements in dot product engines to replicating both synapse and neuron wall behaviors, the memristor has proved a versatile component. Here we demonstrate an analog mode of operation observed in our silicon oxide memristors and apply this to the problem of edge detection. We demonstrate how a potential divider exploiting this analog behavior can prove a scalable solution to edge detection. We confirm its behavior experimentally and simulate its performance on a standard testbench. We show good performance comparable to existing memristor based work with a benchmark score of 0.465 on the BSDS500 dataset, while simultaneously maintaining a lower component count. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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