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Rapid application prototyping for hardware modular spiking neural network architectures

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Abstract

Spiking neural networks (SNNs) are well suited for functions such as data/pattern classification, estimation, prediction, signal processing and robotic control applications. Whereas the real-world embedded applications are often multi-functional with orthogonal or contradicting functional requirements. The EMBRACE hardware modular SNN architecture has been previously reported as an embedded computing platform for complex real-world applications. The EMBRACE architecture employs genetic algorithm (GA) for training the SNN which offers faster prototyping of SNN applications, but exhibits a number of limitations including poor scalability and search space explosions for the evolution of large-scale, complex, real-world applications. This paper investigates the limitations of evolving real-world embedded applications with orthogonal functional goals on hardware SNN using GA-based training. This paper presents a novel, fast and efficient application prototyping technique using the EMBRACE hardware modular SNN architecture and the GA-based evolution platform. Modular design and evolution of a robotic navigational controller application decomposed into obstacle avoidance controller and speed and direction manager application subtasks is presented. The proposed modular evolution technique successfully integrates the orthogonal functionalities of the application and helps to overcome contradicting application scenarios gracefully. Results illustrate that the modular evolution of the application reduces the SNN configuration search space and complexity for the GA-based SNN evolution, offering rapid and successful prototyping of complex applications on the hardware SNN platform. The paper presents validation results of the evolved robotic application implemented on the EMBRACE architecture prototyped on Xilinx Virtex-6 FPGA interacting with the player-stage robotics simulator.

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Notes

  1. EMulating Biologically-inspiRed ArChitectures in hardwarE.

  2. The task decomposition for the robotic navigational controller modular application design presented in this paper has been done manually based on input vector partitioning and analysing the orthogonality of the functional requirements of the application.

  3. The markers for the proposed robotic navigational controller application are chosen by the designer such that the robot can progress towards the destination by following the markers in sequence. The markers can also be chosen by applying meta-heuristic algorithms to the classical Travelling Salesman Problem which is currently out of scope of this paper.

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Acknowledgments

This research is supported by the International Centre for Graduate Education in Micro and Nano-Engineering (ICGEE), Irish Research Council for Science, Engineering and Technology (IRCSET) and Xilinx University Programme.

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Authors and Affiliations

  1. Bio-Inspired Electronics and Reconfigurable Computing, National University of Ireland, Galway, Ireland

    Sandeep Pande, Fearghal Morgan, Finn Krewer & Brian McGinley

  2. Intelligent Systems Research Centre, University of Ulster, Derry, Northern Ireland, UK

    Jim Harkin & Liam McDaid

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  1. Sandeep Pande
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  2. Fearghal Morgan
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  3. Finn Krewer
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  4. Jim Harkin
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  5. Liam McDaid
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  6. Brian McGinley
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Correspondence to Sandeep Pande.

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Pande, S., Morgan, F., Krewer, F. et al. Rapid application prototyping for hardware modular spiking neural network architectures. Neural Comput & Applic 28, 2767–2779 (2017). https://doi.org/10.1007/s00521-015-2136-0

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