14 citations · 18 across the 7 of their papers we have counts for
9 papers
How to Train Your Resistive Network: Generalized Equilibrium Propagation and Analytical Learning
Jonathan Lin, Aman Desai, Frank Barrows +1
Machine learning is a powerful method of extracting meaning from data; unfortunately, current digital hardware is extremely energy-intensive. There is interest in an alternative an…
A unifying approach to self-organizing systems interacting via conservation laws
Frank Barrows, Guanming Zhang, Satyam Anand +5
We present a unified framework for embedding and analyzing dynamical systems using generalized projection operators rooted in local conservation laws. By representing physical, bio…
Voltage-Controlled Oscillator and Memristor-Based Analog Computing for Solving Systems of Linear Equations
Hao Li, Rizwan S. Peerla, Frank Barrows +2
Matrix computations have become increasingly significant in many data-driven applications. However, Moores law for digital computers has been gradually approaching its limit in rec…
R-Ising: Effective resistance in random magnetic nanowires networks
Frank Barrows, Ezio Iacocca, Francesco Caravelli
Random assemblies of magnetic nanowires represent a unique class of materials with promising applications in spintronics and information storage. These assemblies exhibit complex b…
Uncontrolled learning: co-design of neuromorphic hardware topology for neuromorphic algorithms
Frank Barrows, Jonathan Lin, Francesco Caravelli +1
Hardware-based neuromorphic computing remains an elusive goal with the potential to profoundly impact future technologies and deepen our understanding of emergent intelligence. The…
Memristive Linear Algebra
Jonathan Lin, Frank Barrows, Francesco Caravelli
The advent of memristive devices offers a promising avenue for efficient and scalable analog computing, particularly for linear algebra operations essential in various scientific a…
Quantum graph models for transport in filamentary switching
Alison A. Silva, Fabiano M. Andrade, Francesco Caravelli
The formation of metallic nanofilaments bridging two electrodes across an insulator is a mechanism for resistive switching. Examples of such phenomena include atomic synapses, whic…
Network analysis of memristive device circuits: dynamics, stability and correlations
Frank Barrows, Forrest C. Sheldon, Francesco Caravelli
Networks with memristive devices are a potential basis for the next generation of computing devices. They are also an important model system for basic science, from modeling nanosc…
Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks
Valentina Baccetti, Ruomin Zhu, Zdenka Kuncic +1
Networks composed of nanoscale memristive components, such as nanowire and nanoparticle networks, have recently received considerable attention because of their potential use as ne…