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Design and Implementation of Digital Spiking Neurons for Ultra-low-Power In-cluster processors
KTH, School of Information and Communication Technology (ICT).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Neuromorphic computing is a recent and growing field of research. Its conceptual attractiveness is due to the potential it has in deep learning applications such as sensor networks, low-power computer vision, robotics and other fields. Inspired by the functioning of brain, different neural network models have been devised, each with their own special focus on certain applications. Using such computing models are already helping us in different cases such as image, character and voice recognition, data analysis, stock market prediction, etc.

Among the multitude of artificial neural models available, spiking neurons are more deeply inspired by biological neural networks. Leaky, Integrate and Fire (LIF) neuron model is one such model that can reproduce a good number of functions, be simple and also extensible in structure. Current deep learning applications are tied to servers and datacenters for their power and resource hungry existence. This work aims at building a low power neuron core taking advantage of LIF neuron, that could possible result in independent battery powered devices. A hardware design of LIF neuron based scalable neural core is explored, constructed and analysis for power consumption is made.

Place, publisher, year, edition, pages
2016. , p. 66
Series
TRITA-ICT-EX ; 2016:191
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:kth:diva-198115OAI: oai:DiVA.org:kth-198115DiVA, id: diva2:1055658
External cooperation
ETH Zurich
Subject / course
System-on-Chip
Educational program
Master of Science - System-on-Chip Design
Presentation
2016-09-15, 14:00 (English)
Examiners
Available from: 2017-01-25 Created: 2016-12-13 Last updated: 2017-04-25Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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