76 citations · 78 across the 5 of their papers we have counts for
5 papers
Nonideality-aware training makes memristive networks more robust to adversarial attacks
Dovydas Joksas, Luis Muñoz-González, Emil Lupu +1
Neural networks are now deployed in a wide number of areas from object classification to natural language systems. Implementations using analog devices like memristors promise bett…
Emerging Nonvolatile Memories for Machine Learning
Adnan Mehonic, Dovydas Joksas
Digital computers have been getting exponentially faster for decades, but huge challenges exist today. Transistor scaling, described by Moore's law, has been slowing down over the…
Memristive, Spintronic, and 2D-Materials-Based Devices to Improve and Complement Computing Hardware
Dovydas Joksas, AbdulAziz AlMutairi, Oscar Lee +5
In a data-driven economy, virtually all industries benefit from advances in information technology -- powerful computing systems are critically important for rapid technological pr…
Nonideality-Aware Training for Accurate and Robust Low-Power Memristive Neural Networks
Dovydas Joksas, Erwei Wang, Nikolaos Barmpatsalos +4
Recent years have seen a rapid rise of artificial neural networks being employed in a number of cognitive tasks. The ever-increasing computing requirements of these structures have…
Committee machines -- a universal method to deal with non-idealities in memristor-based neural networks
D. Joksas, P. Freitas, Z. Chai +7
Artificial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergen…