Spiking neural networks (SNNs) are well suitedfor functions such as data/pattern classification, estimation,prediction, signal processing and robotic control applications.Whereas the real-world embedded applications areoften multi-functional with orthogonal or contradictingfunctional requirements. The EMBRACE hardware modularSNN architecture has been previously reported as anembedded computing platform for complex real-worldapplications. The EMBRACE architecture employs geneticalgorithm (GA) for training the SNN which offers fasterprototyping of SNN applications, but exhibits a number oflimitations including poor scalability and search spaceexplosions for the evolution of large-scale, complex, realworldapplications. This paper investigates the limitationsof evolving real-world embedded applications withorthogonal functional goals on hardware SNN using GAbasedtraining. This paper presents a novel, fast and effi-cient application prototyping technique using theEMBRACE hardware modular SNN architecture and theGA-based evolution platform. Modular design and evolutionof a robotic navigational controller applicationdecomposed into obstacle avoidance controller and speedand direction manager application subtasks is presented.The proposed modular evolution technique successfullyintegrates the orthogonal functionalities of the applicationand helps to overcome contradicting application scenariosgracefully. Results illustrate that the modular evolution ofthe application reduces the SNN configuration search spaceand complexity for the GA-based SNN evolution, offeringrapid and successful prototyping of complex applications onthe hardware SNN platform. The paper presents validationresults of the evolved robotic application implemented onthe EMBRACE architecture prototyped on Xilinx Virtex-6FPGA interacting with the player-stage robotics simulator.
- Spiking neural networks
- ring topology
Pande, S., Morgan, F., Krewer, F., Harkin, J., McDaid, LJ., & McGinley, B. (2016). Rapid application prototyping for hardware modular spiking neural network architectures. Neural Computing and Applications, 27(4). https://doi.org/10.1007/s00521-015-2136-0