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20182022
most citedSpiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective

23 citations · 32 across the 7 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.ET2019

Error-triggered Three-Factor Learning Dynamics for Crossbar Arrays

Melika Payvand, Mohammed Fouda, Fadi Kurdahi +2

Recent breakthroughs suggest that local, approximate gradient descent learning is compatible with Spiking Neural Networks (SNNs). Although SNNs can be scalably implemented using ne…

cs.ET2019★ 23 cited

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective

M. E. Fouda, F. Kurdahi, A. Eltawil +1

On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adapta…

cs.DC2019★ 2 cited

The Information Processing Factory: Organization, Terminology, and Definitions

Eberle A. Rambo, Bryan Donyanavard, Minjun Seo +9

The Information Processing Factory (IPF) project has recently introduced the abstraction of complex architectures as self-aware information processing factories. These factories co…

cs.IT2019

Non-Stationary Polar Codes for Resistive Memories

Marwen Zorgui, Mohammed E. Fouda, Zhiying Wang +2

Resistive memories are considered a promising memory technology enabling high storage densities with in-memory computing capabilities. However, the readout reliability of resistive…

cs.ET2019★ 4 cited

On Resistive Memories: One Step Row Readout Technique and Sensing Circuitry

Mohammed E Fouda, Ahmed M. Eltawil, Fadi Kurdahi

Transistor-based memories are rapidly approaching their maximum density per unit area. Resistive crossbar arrays enable denser memory due to the small size of switching devices. Ho…