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An adaptive efficient memristive ink drop spread (IDS) computing system

  • Sajad Haghzad Klidbary
  • Saeed Bagheri Shouraki
  • Iman Esmaili Paeen Afrakoti
Original Article
  • 43 Downloads

Abstract

Active Learning Method (ALM) is one of the powerful tools in soft computing and it is inspired by the human brain capabilities in approaching complicated problems. ALM, which is in essence an adaptive fuzzy learning algorithm, tries to model a Multi-Input Single-Output system with several single-input single-output subsystems. Each of these subsystems is then modeled by an ink drop spread (IDS) plane. IDS operator, which is the main processing engine of ALM, extracts two kinds of informative features, Narrow Path and Spread, from each IDS plane without complicated computations. These features from all IDS planes are then aggregated in the inference engine. Despite the great performance of ALM in different applications, an efficient hardware implementation has remained a challenge, which is mainly due to considerably high memory requirement of IDS operation. In this paper, in a novel approach to IDS operation, we propose an abstract representation of the IDS planes which minimizes the memory requirement and the computational cost, and consequently, benefits the hardware implementation in terms of area and speed. The proposed approach is fully compatible with memristor-crossbar implementation with an adaptive learning capability. Simpler learning algorithm and higher speed make our proposed algorithm suitable for applications where real-time process, low-cost and small implementation are of high priority. Applications in the classification of real-world datasets and function approximation are provided to confirm the effectiveness of the algorithm. Eventually, the paper concludes that the proposed computing structure provides a synergy between artificial neural networks and fuzzy domains.

Keywords

Active learning method (ALM) Memristor-crossbar/CMOS Function approximation Classification 

Notes

Acknowledgments

The authors would like to thank Soroush Sheikhpour Kourabbaslou and Mohammad Bavandpour for their kind discussions. The first author is grateful to Iran National Science Foundation (INSF), which has partially supported the present research (Grant No. 96000943).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Artificial Creatures Laboratory (ACL), Research Group for Brain Simulation and Cognitive Science, Department of Electrical EngineeringSharif University of TechnologyTehranIran
  2. 2.Faculty of Engineering and TechnologyUniversity of MazandaranBabolsarIran

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