A study on supporting spiking neural network models based on multiple neuromorphic processors
- Authors
- Hong B.; Cho J.; Kim B.; Min H.; Hong J.; Lee K.M.
- Issue Date
- Sep-2019
- Publisher
- Association for Computing Machinery, Inc
- Keywords
- Neuromorphic computing; Neuromorphic porcessor; Spiking neural networks
- Citation
- Proceedings of the 2019 Research in Adaptive and Convergent Systems, RACS 2019, pp.131 - 132
- Journal Title
- Proceedings of the 2019 Research in Adaptive and Convergent Systems, RACS 2019
- Start Page
- 131
- End Page
- 132
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/35492
- DOI
- 10.1145/3338840.3355692
- ISSN
- 0000-0000
- Abstract
- Neuromorphic computing or neuromorphic engineering is an engineering discipline that attempts to simulate human brain function by creating circuits that mimic the shape of neurons. In the field of neuromorphic computing, neuromorphic processors are used. There are many types of neuromorphic processors, and there are neuromorphic processors implemented based on FPGAS. Neuromorphic processors use an artificial intelligence model called a spiking neural network. Each neuromorphic processor has different characteristics. For example, the spiking neural network model supported by each supported neuromorphic processor may be different. In this paper, we propose a service architecture named NAAL (Neuromorphic Architecture Abstract Layer) that enables the use of spiking neural networks by virtualizing various neuromorphic processors with different characteristics. © 2019 Association for Computing Machinery.
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