Design and Mathematical Modelling of Inter Spike Interval of Temporal Neuromorphic Encoder for Image Recognition

Neuromorphic computing systems emulate the electrophysiological behavior of the biological nervous system using mixed-mode analog or digital VLSI circuits. These systems show superior accuracy and power efficiency in carrying out cognitive tasks. The neural network architecture used in neuromorphic...

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Bibliographic Details
Published inarXiv.org
Main Authors Aadhitiya, V S, Jani Babu Shaik, Singhal, Sonal, Siona Menezes Picardo, Goel, Nilesh
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.05.2022
Subjects
Analog circuits
Coders
Cognitive tasks
Computer architecture
Image processing
Integrated circuits
Intervals
Mathematical analysis
Mathematical models
Nervous system
Neural networks
Neuromorphic computing
Object recognition
Pixels
Power efficiency
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Summary:Neuromorphic computing systems emulate the electrophysiological behavior of the biological nervous system using mixed-mode analog or digital VLSI circuits. These systems show superior accuracy and power efficiency in carrying out cognitive tasks. The neural network architecture used in neuromorphic computing systems is spiking neural networks (SNNs) analogous to the biological nervous system. SNN operates on spike trains as a function of time. A neuromorphic encoder converts sensory data into spike trains. In this paper, a low-power neuromorphic encoder for image processing is implemented. A mathematical model between pixels of an image and the inter-spike intervals is also formulated. Wherein an exponential relationship between pixels and inter-spike intervals is obtained. Finally, the mathematical equation is validated with circuit simulation.
ISSN:2331-8422