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  • Por favor, use este identificador para citar o enlazar este ítem: https://repositorio.uti.edu.ec//handle/123456789/3446
    Título : Efficient Configuration for a Scalable Spiking Neural Network Platform by means of a Synchronous Address Event Representation bus
    Autor : Zapata, Mireya
    Jadán-Guerrero, Janio
    Madrenas, Jordi
    Fecha de publicación : 2018
    Editorial : 2018 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2018. Pages 241 - 248
    Resumen : Hardware architectures for Spiking Neural Networks (SNNs) emulation exhibit accelerated processing thanks to their massive parallelism. However, configuring multichip platforms and setting up a neural application can be an abstract and rigid procedure. In this paper, a simple and efficient centralized configuration solution for a scalable multichip platform based on FPGA and PSoC devices is presented. For this purpose, a dedicated Master Device (MD) node has been used to configure a scalable network of Neuromorphic Devices (NDs). The NDs are general purpose devices which can be programmed to execute any neural algorithm based on spikes with a customized synapse topology. In the proposed approach, the communication channel is re-utilized and the Address Representation Event (AER) protocol modified to configure the entire system. This approximation allows achieving area and power consumption optimization since it eliminates the need to implement a specific instance per chip. Simulations shown demonstrate the performance and temporal characterization of this proposal. © 2018 IEEE.
    URI : https://ieeexplore.ieee.org/document/8541463
    http://repositorio.uti.edu.ec//handle/123456789/3446
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