Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Date
2019-02-28Online Publication Date
2019-02-28Print Publication Date
2018-07Permanent link to this record
http://hdl.handle.net/10754/652973
Metadata
Show full item recordAbstract
Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar structure with the synaptic sampling cell (SSC) being used as a basic stochastic unit. The increase in the edge computing devices in the Internet of things era, drives the need for hardware acceleration for data processing and computing. The computational considerations of the processing speed and possibility for the real-time realization pushes the synaptic sampling algorithm that demonstrated promising results on software for hardware implementation.Citation
Dolzhikova I, Salama K, Kizheppatt V, James A (2018) Memristor-Based Synaptic Sampling Machines. 2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO). Available: http://dx.doi.org/10.1109/NANO.2018.8626382.Sponsors
We would like to express special thanks to Rawan Nouz for the initial ideas, technical support and useful discussions on the topic of synaptic sampling. In addition to that, we thank Umesh Chand and ulrich Buttner for guidance and support.Conference/Event name
18th International Conference on Nanotechnology, NANO 2018arXiv
1808.00679ae974a485f413a2113503eed53cd6c53
10.1109/NANO.2018.8626382