Memristor-based Synaptic Sampling Machines

Irina Dolzhikova, Khaled Salama, Vipin Kizheppatt, Alex Pappachen James

Research output: Contribution to journalArticle

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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.
Original languageUndefined/Unknown
JournalIEEE NANO,
Publication statusPublished - Aug 2 2018

Keywords

  • cs.ET
  • cs.AI

Cite this

Dolzhikova, I., Salama, K., Kizheppatt, V., & James, A. P. (2018). Memristor-based Synaptic Sampling Machines. IEEE NANO,.

Memristor-based Synaptic Sampling Machines. / Dolzhikova, Irina; Salama, Khaled; Kizheppatt, Vipin; James, Alex Pappachen.

In: IEEE NANO, 02.08.2018.

Research output: Contribution to journalArticle

Dolzhikova, I, Salama, K, Kizheppatt, V & James, AP 2018, 'Memristor-based Synaptic Sampling Machines' IEEE NANO,.
Dolzhikova I, Salama K, Kizheppatt V, James AP. Memristor-based Synaptic Sampling Machines. IEEE NANO,. 2018 Aug 2.
Dolzhikova, Irina ; Salama, Khaled ; Kizheppatt, Vipin ; James, Alex Pappachen. / Memristor-based Synaptic Sampling Machines. In: IEEE NANO,. 2018.
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