23 citations · 32 across the 7 of their papers we have counts for
10 papers
In-memory Associative Processors: Tutorial, Potential, and Challenges
Mohammed E. Fouda, Hasan Erdem Yantir, Ahmed M. Eltawil +1
In-memory computing is an emerging computing paradigm that overcomes the limitations of exiting Von-Neumann computing architectures such as the memory-wall bottleneck. In such para…
Efficient Noise Mitigation Technique for Quantum Computing
Ali Shaib, Mohamad H. Naim, Mohammed E. Fouda +2
Quantum computers have enabled solving problems beyond the current computers' capabilities. However, this requires handling noise arising from unwanted interactions in these system…
Resistive Neural Hardware Accelerators
Kamilya Smagulova, Mohammed E. Fouda, Fadi Kurdahi +2
Deep Neural Networks (DNNs), as a subset of Machine Learning (ML) techniques, entail that real-world data can be learned and that decisions can be made in real-time. However, their…
On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networks
Melika Payvand, Mohammed E. Fouda, Fadi Kurdahi +2
Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity. Althou…
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…
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…