Memristor-Based Edge Detection for Spike Encoded Pixels
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.
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REFERENCES
- https://doi.org//10.1038/ncomms3072
- https://doi.org//10.1109/TPAMI.2010.161
- https://doi.org//10.3389/fnins.2016.00482
- https://doi.org//10.3389/fncom.2015.00099
- https://doi.org//10.1109/TNN.2005.860850
- https://doi.org//10.1109/IJCNN.2012.6252600
- https://doi.org//10.1039/c8fd00118a
- https://doi.org//10.1038/s41928-017-0002-z
- https://doi.org//10.1109/TVLSI.2014.2359801
- https://doi.org//10.3389/fnins.2019.00593
- https://doi.org//10.3389/fnins.2016.00057
- https://doi.org//10.1016/j.mee.2017.04.033
- https://doi.org//10.1007/s11554-012-0254-9
- https://doi.org//10.1063/1.1874313
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AUTHORS (4)
CATEGORIES
- Radiology and Organ Imaging
- Decision Making
- Clinical Nursing: Tertiary (Rehabilitative)
- Image Processing
- Autonomic Nervous System
- Cellular Nervous System
- Biological Engineering
- Sensory Systems
- Central Nervous System
- Neuroscience
- Endocrinology
- Artificial Intelligence and Image Processing
- Signal Processing
- Rehabilitation Engineering
- Biomedical Engineering not elsewhere classified
- Stem Cells
- Neurogenetics
- Developmental Biology