Memristor-based Synaptic Sampling Machines

King Abdullah University of Science and Technology, Thuwal, Makkah Province, Saudi Arabia Abstract-- 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. Biological neural networks are extensively studied in the past century with an aim to mimic the human intelligence to build machine having abilities of cognition, perception and consciousness.