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A Digital Neuromorphic Hardware for Spiking Neural Network
- Source :
- 2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC).
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- The neuromorphic hardware with a non-von Neumann architecture has the advantage of highly-parallel and low-power. In this paper, a digital neuromorphic core with 1024 neurons, 1024 axons and a $1024\times 1024$ synaptic crossbar is designed, and the scalable network could be implemented based on the 2D mesh network on chip (NOC) architecture. The transformed deep spiking neural network (SNN) models can be mapped to our hardware directly, and show good application results. At the case of the full firing rate, the average power of a spike is 2.76E-08J, and for some image recognition tasks, the hardware power consumption is at the milliwatt level.
- Subjects :
- Spiking neural network
Computer science
business.industry
02 engineering and technology
020202 computer hardware & architecture
Power (physics)
Network on a chip
Neuromorphic engineering
Scalability
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Spike (software development)
Crossbar switch
business
Computer hardware
Neuromorphic hardware
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC)
- Accession number :
- edsair.doi...........87498eef4bb902e75faa373ae9f4f2f5
- Full Text :
- https://doi.org/10.1109/edssc.2019.8754093