Thermodynamic-RAM technology stack

The perceptron: A probabilistic model for information storage and organization in the brain.

Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280 · see citing works

Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations

Benjamin, B. V., Gao, P., McQuinn, E., Choudhary, S., Chandrasekaran, A. R., Bussat, J.-M., Alvarez-Icaza, R., Arthur, J. V., Merolla, P. A., & Boahen, K. A. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2313565 · Cited by 1,270 · see citing works

Neuromorphic computing using non-volatile memory

Burr, G. W., Shelby, R. M., Sebastian, A., Kim, S., Kim, S., Sidler, S., Virwani, K. R., Ishii, M., Narayanan, P., Fumarola, A., et al. (2016) · Advances in Physics X · doi:10.1080/23746149.2016.1259585 · Cited by 1,158 · see citing works

Machine Learning: An Algorithmic Perspective

Marsland, S. (2009) · Cited by 1,075 · see citing works

Achieving Human Parity in Conversational Speech RecognitionPreprint

Xiong, W., Droppo, J., Huang, X.-D., Seide, F., Seltzer, M., Stolcke, A., Yu, D., & Zweig, G. (2016) · arXiv.org · doi:10.48550/arxiv.1610.05256 · Cited by 600 · see citing works

Memristor-based neural networks

Thomas, A. (2013) · Journal of Physics D: Applied Physics · doi:10.1088/0022-3727/46/9/093001 · Cited by 471 · see citing works

Synaptic behaviors and modeling of a metal oxide memristive device

Chang, T., Jo, S.-H., Kim, K.-H., Sheridan, P., Gaba, S., & Lu, W. (2011) · Applied Physics A · doi:10.1007/s00339-011-6296-1 · Cited by 432 · cited 1× · see citing works

The NIH BRAIN Initiative

Insel, T. R., Landis, S. C., & Collins, F. S. (2013) · Science · doi:10.1126/science.1239276 · Cited by 342 · see citing works

Complementary resistive switching in tantalum oxide-based resistive memory devices

Yang, Y., Sheridan, P., & Lu, W. (2012) · Applied Physics Letters · doi:10.1063/1.4719198 · Cited by 238 · see citing works

SPICE modeling of memristive, memcapacitative and meminductive systems

Biolek, D., Biolek, Z., & Biolková, V. (2009) · European Conference on Circuit Theory and Design · doi:10.1109/ecctd.2009.5274934 · Cited by 201 · see citing works

Cation-based resistance change memory

Valov, I., & Kozicki, M. N. (2013) · Journal of Physics D Applied Physics · doi:10.1088/0022-3727/46/7/074005 · Cited by 198 · see citing works

Simulation of multilevel switching in electrochemical metallization memory cells

Menzel, S., Böttger, U., & Waser, R. M. (2012) · Journal of Applied Physics · doi:10.1063/1.3673239 · Cited by 166 · see citing works

Nanoscale electronic synapses using phase change devices

Jackson, B. L., Rajendran, B., Corrado, G. S., Breitwisch, M., Burr, G. W., Cheek, R. W., Gopalakrishnan, K., Raoux, S., Rettner, C. T., Padilla, A., et al. (2013) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/2463585.2463588 · Cited by 163 · see citing works

Deep Learning on FPGAs: Past, Present, and FuturePreprint

Lacey, G., Taylor, G. W., & Areibi, S. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04283 · Cited by 157 · see citing works

Universal Memcomputing Machines

Traversa, F. L., & Ventra, M. D. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2391182 · Cited by 150 · see citing works

Self-directed channel memristor for high temperature operation

Campbell, K. A. (2017) · Microelectronics Journal · doi:10.1016/j.mejo.2016.11.006 · Cited by 148 · cited 1× · see citing works

Cognitive computing systems: Algorithms and applications for networks of neurosynaptic cores

Esser, S. K., Andreopoulos, A., Appuswamy, R., Datta, P., Barch, D., Amir, A., Arthur, J., Cassidy, A., Flickner, M., Merolla, P., et al. (2013) · The 2013 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6706746 · Cited by 125 · see citing works

Silver chalcogenide based memristor devices

Oblea, A. S., Timilsina, A., Moore, D., & Campbell, K. A. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596775 · Cited by 118 · see citing works

Device and SPICE modeling of RRAM devices

Sheridan, P., Kim, K.-H., Gaba, S., Chang, T., Chen, L., & Lu, W. (2011) · Nanoscale · doi:10.1039/c1nr10557d · Cited by 98 · see citing works

AHaH Computing–From Metastable Switches to Attractors to Machine Learning

Nugent, M. A., & Molter, T. W. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085175 · Cited by 55 · see citing works

Memristive operations demonstrated by gap-type atomic switches

Hasegawa, T., Nayak, A., Ohno, T., Terabe, K., Tsuruoka, T., Gimzewski, J. K., & Aono, M. (2011) · Applied Physics A · doi:10.1007/s00339-011-6317-0 · Cited by 45 · see citing works

2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

(2017) · doi:10.1109/isvlsi40576.2017 · Cited by 45 · see citing works

Resistance Drift Model for Conductive-Bridge (CB) RAM by Filament Surface Relaxation

Choi, S., Ambrogio, S., Balatti, S., Nardi, F., & Ielmini, D. (2012) · IEEE International Memory Workshop (IMW) · doi:10.1109/imw.2012.6213666 · Cited by 12 · see citing works

SpiNNaker: Fault tolerance in a power- and area- constrained large-scale neuromimetic architecture

Navaridas, J., Furber, S. B., Garside, J. D., Jin, X., Khan, M. M., Lester, D., Luján, M., Miguel-Alonso, J., Painkras, E., Patterson, C., et al. (2013) · Parallel Computing · doi:10.1016/j.parco.2013.09.001 · Cited by 10 · see citing works

European-Led Project Strives to Simulate the Human Brain

Hampton, T. (2014) · JAMA · doi:10.1001/jama.2014.3839 · Cited by 7 · see citing works

Dielectrophoretic Architectures

Wissner‐Gross, A. D. (2009) · doi:10.1002/9780470429983.ch5 · Cited by 2 · see citing works

Applying Cognitive Memory to CyberSecurity

McCormick, B. (2013) · Advances in information security · doi:10.1007/978-1-4614-7597-2_4 · Cited by 1 · see citing works

Memristors for the Curious Outsiders

Carbajal, J. P., & Caravelli, F. (2018) · Technologies · doi:10.3390/technologies6040118 · Cited by 64 · see citing works

The Generalized Metastable Switch Memristor ModelPreprint

Molter, T. W., & Nugent, M. A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.04659 · Cited by 50 · see citing works

All-Printed Flexible Memristor with Metal–Non-Metal-Doped TiO2 Nanoparticle Thin Films

Khan, M., Rehman, H. M. M. U., Tehreem, R., Saqib, M., Rehman, M. M., & Kim, W. Y. (2022) · Nanomaterials · doi:10.3390/nano12132289 · Cited by 41 · see citing works

Exploring Memristor Multi-Level Tuning Dependencies on the Applied Pulse Properties via a Low Cost Instrumentation Setup

Gomez, J. T., Vourkas, I., & Hoffman, A. C. A. (2019) · IEEE Access · doi:10.1109/access.2019.2915100 · Cited by 35 · see citing works

Memristive Logic in Crossbar Memory Arrays: Variability-Aware Design for Higher Reliability

Escudero, M., Vourkas, I., Rubio, A., & Moll, F. (2019) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2019.2923731 · Cited by 29 · see citing works

Voltage Divider for Self-Limited Analog State Programing of Memristors

Gomez, J. T., Vourkas, I., Hoffman, A. C. A., Sirakoulis, G. C., & Rubio, A. (2019) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2019.2923716 · Cited by 20 · see citing works

Comprehensive predictive modeling of resistive switching devices using a bias-dependent window function approach

Fernández, C., Gomez, J. T., Ortiz, J., & Vourkas, I. (2020) · Solid-State Electronics · doi:10.1016/j.sse.2020.107833 · Cited by 16 · see citing works

Exploring the “resistance change per energy unit” as universal performance parameter for resistive switching devices

Gomez, J. T., Vourkas, I., Hoffman, A. C. A., Rodriguez, R., Martin-Martinez, J., Nafria, M., & Rubio, A. (2019) · Solid-State Electronics · doi:10.1016/j.sse.2019.107748 · Cited by 10 · see citing works

Stochastic Resonance Exploration in Current-driven ReRAM Devices

Cirera, A., Vourkas, I., Rubio, A., & Pérez, M. A. (2022) · IEEE International Conference on Nanotechnology (IEEE-NANO) · doi:10.1109/nano54668.2022.9928748 · Cited by 8 · see citing works

Exploring Different Circuit-level Approaches to the Forming of Resistive Random Access Memories

Cirera, A., Fernández, C., Vourkas, I., & Rubio, A. (2022) · International Conference on Modern Circuits and Systems Technologies · doi:10.1109/mocast54814.2022.9837684 · Cited by 7 · see citing works

ReRAM-based Ratioed Combinational Circuit Design: a Solution for in-Memory Computing

Fernández, C., & Vourkas, I. (2020) · International Conference on Modern Circuits and Systems Technologies · doi:10.1109/mocast49295.2020.9200279 · Cited by 7 · see citing works

Experimental measurements on resistive switching devices: Gaining hands-on experience

Gomez, J. T., Hoffman, A. C. A., Vourkas, I., & Sirakoulis, G. C. (2018) · International Conference on Modern Circuits and Systems Technologies · doi:10.1109/mocast.2018.8376607 · Cited by 5 · see citing works

Reliability-Aware Ratioed Logic Operations for Energy-Efficient Computational ReRAM

Fernández, C., & Vourkas, I. (2022) · IFIP/IEEE International Conference on Very Large Scale Integration · doi:10.1109/vlsi-soc54400.2022.9939627 · Cited by 5 · see citing works

Design and Simulation of Peripheral Driving Circuitry for Computational ReRAM

Fernández, C., Vourkas, I., & Rubio, A. (2022) · Conference on Design of Circuits and Integrated Systems · doi:10.1109/dcis55711.2022.9970082 · Cited by 5 · see citing works

On the Variability-aware Design of Memristor-based Logic Circuits

Escudero, M., Vourkas, I., Rubio, A., & Moll, F. (2018) · IEEE International Conference on Nanotechnology (IEEE-NANO) · doi:10.1109/nano.2018.8626367 · Cited by 5 · see citing works

RevI-Ve: A Comprehensive Software Interface for Easy ReRAM Device Characterization

Riquelme, J., Melivilu, M., Vourkas, I., & Cirera, A. (2023) · International Conference on Modern Circuits and Systems Technologies · doi:10.1109/mocast57943.2023.10176900 · Cited by 3 · see citing works

Shortest Path Computing in Directed Graphs with Weighted Edges Mapped on Random Networks of Memristors

Fernández, C., Vourkas, I., & Rubio, A. (2020) · Parallel Processing Letters · doi:10.1142/s0129626420500024 · Cited by 3 · see citing works

A Voltage-Driven Window Function Concept for Behavioral Memristor Device Modeling

Fernández, C., Ortiz, J., & Vourkas, I. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9180664 · Cited by 2 · see citing works

The Effects of Conductance on Metastable Switches in Memristive Devices Based on Anti-Hebbian and Hebbian (AHaH) Learning Rules

Abbood, A. D., Hasan, A. A., & Attea, B. A. (2021) · Iraqi Journal of Science · doi:10.24996/ijs.2021.62.10.31 · Cited by 1 · see citing works

From memristors to compositional machine learning

Nugent, A., & Molter, T. (2017) · Proceedings of the Neuromorphic Computing Symposium · doi:10.1145/3183584.3183615

Machine Learning with Memristors via Thermodynamic RAMPreprint

Molter, T. W., & Nugent, M. A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.04105

Performance Assessment of Memristor Networks as Shortest Path Problem Solvers

Fernández, C., & Vourkas, I. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9181109

Mathematical simulation of memristive for classification in machine learning

Hasan, A. A., Abdullah, N. A. Z., & Abbood, A. D. (2022) · Periodicals of Engineering and Natural Sciences (PEN) · doi:10.21533/pen.v10i2.2793

Using Current to Drive Two SDC Memristors Connected in Series and in Anti-Series

Cirera, A., Miribel‐Català, P. L., Rubio, A., Garrido, B., Colomer‐Farrarons, J., & Vourkas, I. (2023) · Conference on Design of Circuits and Integrated Systems · doi:10.1109/dcis58620.2023.10335996

Thermodynamic-RAMPreprint

Stack, T., Nugent, M. A., & Molter, T. W. (2016)

Cortical Processing with Thermodynamic-RAMPreprint

Nugent, M. A., & Molter, T. W. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.3215

Machine Learning with Memristors via Thermodynamic RAMPreprint

Molter, T. W., & Nugent, M. A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.04105

AHaH computing with thermodynamic RAM: bridging the technology stack

Nugent, A. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2053381

RRAM-Based Neuromorphic Devices for Artificial-Intelligence Hardware: Device Physics, Materials, Processing, and Packaging

Pyo, S. G. (2026) · Micromachines · doi:10.3390/mi17091032