Emulation of neural networks on a nanoscale architecture

Mary M. Eshaghian-Wilner, Aaron Friesz, Alex Khitun, Shiva Navab, Alice C. Parker, Kang L. Wang, Chongwu Zhou

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


In this paper, we propose using a nanoscale spin-wave-based architecture for implementing neural networks. We show that this architecture can efficiently realize highly interconnected neural network models such as the Hopfield model. In our proposed architecture, no point-to-point interconnection is required, so unlike standard VLSI design, no fan-in/fan-out constraint limits the interconnectivity. Using spin-waves, each neuron could broadcast to all other neurons simultaneously and similarly a neuron could concurrently receive and process multiple data. Therefore in this architecture, the total weighted sum to each neuron can be computed by the sum of the values from all the incoming waves to that neuron. In addition, using the superposition property of waves, this computation can be done in O(1) time, and neurons can update their states quite rapidly.

Original languageEnglish
Article number058
Pages (from-to)288-292
Number of pages5
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 2007 Apr 1

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


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