Record link: 
http://hdl.handle.net/20.500.12708/191927
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Title: 
Embedded Neuromorphic Using Intel’s Loihi Processor
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Citation: 
Marchisio, A., & Shafique, M. (2023). Embedded Neuromorphic Using Intel’s Loihi Processor. In S. Pasricha & M. Shafique (Eds.), Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Use Cases and Emerging Challenges (pp. 137–172). Springer. https://doi.org/10.1007/978-3-031-39932-9_6
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Publisher DOI: 
10.1007/978-3-031-39932-9_6
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Publication Type: 
Book Contribution - Edited Volume Contribution
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Language: 
English
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Authors: 
Marchisio, Alberto 
Shafique, Muhammad 
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Organisational Unit: 
E191-02 - Forschungsbereich Embedded Computing Systems 
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Published in: 
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Use Cases and Emerging Challenges 
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ISBN: 
978-3-031-39932-9
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Date (published): 
10-Oct-2023
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Number of Pages: 
36
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Publisher: 
Springer, Cham
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Keywords: 
spiking neural networks; neuromorphic architecture; loihi; event-based vision
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Abstract: 
Recently, spiking neural networks (SNNs) have demonstrated great success due to their high-performance and low-energy consumption, which makes them suitable for being implemented on embedded devices, such as neuromorphic chips. This chapter presents an overview of event-based SNNs on neuromorphic hardware and their applications. It provides outlooks on the neuromorphic computing platforms, with a special focus on the Intel Loihi research chip. Afterward, a case study on a “car vs. background” classifier implemented on Loihi is discussed in detail.
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Research Areas: 
Computer Engineering and Software-Intensive Systems: 100%
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Science Branch: 
1020 - Informatik: 100%
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Appears in Collections:
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