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PSoC-Based Real-Time Data Acquisition for a Scalable Spiking Neural Network Hardware Architecture
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PSoC-Based Real-Time Data Acquisition for a Scalable Spiking Neural Network Hardware Architecture
Journal
2018 IEEE 3rd Ecuador Technical Chapters Meeting, ETCM 2018
Date Issued
2018
Author(s)
Zapata, Mireya
Centro de investigación en Mecatrónica y Sistemas Interactivos
Balaji U.K.
Madrenas J.
Type
Conference Paper
DOI
10.1109/ETCM.2018.8580286
URL
https://cris.indoamerica.edu.ec/handle/123456789/9035
Abstract
Data acquisition for monitoring the spiky activity of large-scale SNN hardware architectures are a challenge due to their time constraints, complexity, large logic size, and so on. This paper presents a versatile PSoC-Based Data Acquisition prototype, where a specialized Master Device is used for this purpose. It benefits from the heterogeneous nature of SoC platforms that allows it to host programmable logic together with a hard-core ARM processor integrating memory and a variety of peripherals in a single chip. The presented design enables monitoring the performance of a multi-chip neural network through a single Ethernet interface in a hardware and software co-design, which is combined with an application developed in Python that allows the visualization on the PC of a dynamic raster plot of neural activity. In addition, an example of full platform functionality is shown. © 2018 IEEE.
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Amazon forest landsca...
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