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Open AccessArticle

A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System

Department of Precision Instrument, Tsinghua University, Beijing 100084, China
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Author to whom correspondence should be addressed.
Sensors 2017, 17(9), 1941; https://doi.org/10.3390/s17091941
Received: 27 June 2017 / Revised: 15 August 2017 / Accepted: 21 August 2017 / Published: 23 August 2017
(This article belongs to the Section Physical Sensors)
Artificial Neural Networks (ANNs), including Deep Neural Networks (DNNs), have become the state-of-the-art methods in machine learning and achieved amazing success in speech recognition, visual object recognition, and many other domains. There are several hardware platforms for developing accelerated implementation of ANN models. Since Field Programmable Gate Array (FPGA) architectures are flexible and can provide high performance per watt of power consumption, they have drawn a number of applications from scientists. In this paper, we propose a FPGA-based, granularity-variable neuromorphic processor (FBGVNP). The traits of FBGVNP can be summarized as granularity variability, scalability, integrated computing, and addressing ability: first, the number of neurons is variable rather than constant in one core; second, the multi-core network scale can be extended in various forms; third, the neuron addressing and computing processes are executed simultaneously. These make the processor more flexible and better suited for different applications. Moreover, a neural network-based controller is mapped to FBGVNP and applied in a multi-input, multi-output, (MIMO) real-time, temperature-sensing and control system. Experiments validate the effectiveness of the neuromorphic processor. The FBGVNP provides a new scheme for building ANNs, which is flexible, highly energy-efficient, and can be applied in many areas. View Full-Text
Keywords: artificial neural networks; FPGA; neuromorphic processor; granularity variable; MIMO control artificial neural networks; FPGA; neuromorphic processor; granularity variable; MIMO control
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MDPI and ACS Style

Zhang, Z.; Ma, C.; Zhu, R. A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System. Sensors 2017, 17, 1941. https://doi.org/10.3390/s17091941

AMA Style

Zhang Z, Ma C, Zhu R. A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System. Sensors. 2017; 17(9):1941. https://doi.org/10.3390/s17091941

Chicago/Turabian Style

Zhang, Zhen; Ma, Cheng; Zhu, Rong. 2017. "A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System" Sensors 17, no. 9: 1941. https://doi.org/10.3390/s17091941

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