Image Edge Detection Based on Swarm Intelligence Using Memristive Networks

Recent advancements in the development of memristive devices has opened new opportunities for hardware implementation of new computing models. Researchers have shown the suitability of memristive devices for swarm intelligence algorithms to solve a maze in hardware. In this paper, we utilize swarm i...

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Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems 2018-09, Vol.37 (9), p.1774-1787
Main Authors: Pajouhi, Zoha, Roy, Kaushik
Format: Article
Language:English
Subjects:
AgS memristor
Algorithm design and analysis
Algorithms
ant colony
Ant colony optimization
Circuit design
CMOS
Computational modeling
Edge detection
elements with memory
gap-type memristor
Hardware
Image detection
Image edge detection
image processing
Integrated circuit modeling
memcomputing
memory
Memory devices
memristor model
Memristors
Networks
neural computing
NP-complete
Particle swarm optimization
Silver memristor
stochastic processing
Swarm intelligence
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Summary:Recent advancements in the development of memristive devices has opened new opportunities for hardware implementation of new computing models. Researchers have shown the suitability of memristive devices for swarm intelligence algorithms to solve a maze in hardware. In this paper, we utilize swarm intelligence of memristive networks to perform image edge detection. First, we propose a hardware-friendly algorithm for image edge detection based on ant colony optimization. Second, we implement the image edge detection algorithm using memristive networks. Furthermore, we explain the impact of various parameters of the memristors on the efficacy of the implementation. Our results show 28% improvement in the energy compared to a low power CMOS hardware implementation based on stochastic circuits. Furthermore, our design occupies up to 5\boldsymbol \times less area.
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2017.2775227