TY - GEN
T1 - Memristor-Based Synaptic Sampling Machines
AU - Dolzhikova, I.
AU - Salama, Khaled N.
AU - Kizheppatt, V.
AU - James, A.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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.
PY - 2019/2/28
Y1 - 2019/2/28
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/652973
UR - https://ieeexplore.ieee.org/document/8626382
UR - http://www.scopus.com/inward/record.url?scp=85062294912&partnerID=8YFLogxK
U2 - 10.1109/NANO.2018.8626382
DO - 10.1109/NANO.2018.8626382
M3 - Conference contribution
SN - 9781538653364
BT - 2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO)
PB - IEEE
ER -