KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/652973
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AbstractSynaptic 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.
CitationDolzhikova 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.
SponsorsWe 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 name18th International Conference on Nanotechnology, NANO 2018