Matthew J. Marinella
Sandia National Laboratories
191 Papers
487 Citations
Matthew J. Marinella is an academic researcher from Sandia National Laboratories. The author has contributed to research in topics: Neuromorphic engineering & Computer science. The author has an hindex of 24, co-authored 161 publications.
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Papers
A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing
Yoeri van de Burgt,Ewout Lubberman,Ewout Lubberman,Elliot J. Fuller,Scott T. Keene,Gregório Couto Faria,Gregório Couto Faria,Sapan Agarwal,Matthew J. Marinella,A. Alec Talin,Alberto Salleo +10 more
TL;DR: This work describes an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors, opening a path towards extreme interconnectivity comparable to the human brain.
Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing.
Elliot J. Fuller,Scott T. Keene,Armantas Melianas,Zhongrui Wang,Sapan Agarwal,Yiyang Li,Yaakov Tuchman,Conrad D. James,Matthew J. Marinella,Jianhua Yang,Alberto Salleo,A. Alec Talin +11 more
TL;DR: An ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM) is introduced, enabling linear and symmetric weight updates in parallel over an entire crossbar array at megahertz rates over 109 write-read cycles.
621
Li‐Ion Synaptic Transistor for Low Power Analog Computing
Elliot J. Fuller,Farid El Gabaly,François Léonard,Sapan Agarwal,Steven J. Plimpton,Robin B. Jacobs-Gedrim,Conrad D. James,Matthew J. Marinella,A. Alec Talin +8 more
TL;DR: Nonvolatile redox transistors based upon Li-ion battery materials are demonstrated as memory elements for neuromorphic computer architectures with multi-level analog states, "write" linearity, low-voltage switching, and low power dissipation.
529
High-speed and low-energy nitride memristors
Byung Joon Choi,Byung Joon Choi,Antonio C. Torrezan,John Paul Strachan,Paul G. Kotula,Andrew J. Lohn,Matthew J. Marinella,Zhiyong Li,R. Stanley Williams,Jianhua Yang,Jianhua Yang +10 more
TL;DR: In this article, the formation of an Al-rich conduction channel through the AlN layer is revealed, and the motion of positively charged nitrogen vacancies is likely responsible for the observed switching.
321
Resistive memory device requirements for a neural algorithm accelerator
Sapan Agarwal,Steven J. Plimpton,David Russell Hughart,Alexander H. Hsia,Isaac Richter,Jonathan A. Cox,Conrad D. James,Matthew J. Marinella +7 more
- 24 Jul 2016
TL;DR: A general purpose neural architecture that can accelerate many different algorithms and determine the device properties that will be needed to run backpropagation on the neural architecture is proposed.
209