Emily Lynne D'Alessandro
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Emily Lynne D'Alessandro has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Aidan Alexander Burrowes
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
2 shared publications - Douglas Z. Plummer
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
2 shared publications - Joshua Fleischer
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
2 shared publications - Yingying Wu
Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA
2 shared publications - Alexander M. Heard
Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida 32611, USA
1 shared publication - Alexander M. Heard
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
- 24 March 2025
- Published by MDPI AG in Journal of Low Power Electronics and Applications
- Vol. 15 (2) , 16
2D Spintronics for Neuromorphic Computing with Scalability and Energy Efficiency
The demand for computing power has been growing exponentially with the rise of artificial intelligence (AI), machine learning, and the Internet of Things (IoT). This growth requires unconventional computing primitives that prioritize energy efficiency, while also addressing the critical need for scalability. Neuromorphic computing, inspired by the biological brain, offers a transformative paradigm for addressing these challenges. This review paper provides an overview of advancements in 2D spintronics and device architectures designed for neuromorphic applications, with a focus on techniques such as spin-orbit torque, magnetic tunnel junctions, and skyrmions. Emerging van der Waals materials like CrI3, Fe3GaTe2, and graphene-based heterostructures have demonstrated unparalleled potential for integrating memory and logic at the atomic scale. This work highlights technologies with ultra-low energy consumption (0.14 fJ/operation), high switching speeds (sub-nanosecond), and scalability to sub-20 nm footprints. It covers key material innovations and the role of spintronic effects in enabling compact, energy-efficient neuromorphic systems, providing a foundation for advancing scalable, next-generation computing architectures.
- 24 March 2025
- Published by MDPI AG in Journal of Low Power Electronics and Applications
- Vol. 15 (2) , 16
Top Cited PapersOpen Access2D Spintronics for Neuromorphic Computing with Scalability and Energy Efficiency
The demand for computing power has been growing exponentially with the rise of artificial intelligence (AI), machine learning, and the Internet of Things (IoT). This growth requires unconventional computing primitives that prioritize energy efficiency, while also addressing the critical need for scalability. Neuromorphic computing, inspired by the biological brain, offers a transformative paradigm for addressing these challenges. This review paper provides an overview of advancements in 2D spintronics and device architectures designed for neuromorphic applications, with a focus on techniques such as spin-orbit torque, magnetic tunnel junctions, and skyrmions. Emerging van der Waals materials like CrI3, Fe3GaTe2, and graphene-based heterostructures have demonstrated unparalleled potential for integrating memory and logic at the atomic scale. This work highlights technologies with ultra-low energy consumption (0.14 fJ/operation), high switching speeds (sub-nanosecond), and scalability to sub-20 nm footprints. It covers key material innovations and the role of spintronic effects in enabling compact, energy-efficient neuromorphic systems, providing a foundation for advancing scalable, next-generation computing architectures.
- Open Access
- 13 March 2025
- Published by MDPI AG in Preprints.org
2D Spintronics for Neuromorphic Computing with Scalability and Energy Efficiency
The demand for computing power has been growing exponentially with the rise of artificial intelligence (AI), machine learning, and the Internet of Things (IoT). This growth requires unconventional computing primitives that prioritize energy efficiency, while also addressing the critical need for scalability. Neuromorphic computing, inspired by the biological brain, offers a transformative paradigm for addressing these challenges. This review paper provides an overview of advancements in 2D spintronics and device architectures designed for neuromorphic applications, with a focus on techniques such as spin-orbit torque, magnetic tunnel junctions, and skyrmions. Emerging van der Waals materials like CrI3, Fe3GaTe2, and graphene-based heterostructures have demonstrated unparalleled potential for integrating memory and logic at the atomic scale. This work highlights technologies with ultra-low energy consumption (0.14 fJ/operation), high switching speeds (sub-nanosecond), and scalability to sub-20 nm footprints. It covers key material innovations and the role of spintronic effects in enabling compact, energy-efficient neuromorphic systems, providing a foundation for advancing scalable, next-generation computing architectures.
- 13 March 2025
- Published by MDPI AG in Preprints.org
Open Access2D Spintronics for Neuromorphic Computing with Scalability and Energy Efficiency
The demand for computing power has been growing exponentially with the rise of artificial intelligence (AI), machine learning, and the Internet of Things (IoT). This growth requires unconventional computing primitives that prioritize energy efficiency, while also addressing the critical need for scalability. Neuromorphic computing, inspired by the biological brain, offers a transformative paradigm for addressing these challenges. This review paper provides an overview of advancements in 2D spintronics and device architectures designed for neuromorphic applications, with a focus on techniques such as spin-orbit torque, magnetic tunnel junctions, and skyrmions. Emerging van der Waals materials like CrI3, Fe3GaTe2, and graphene-based heterostructures have demonstrated unparalleled potential for integrating memory and logic at the atomic scale. This work highlights technologies with ultra-low energy consumption (0.14 fJ/operation), high switching speeds (sub-nanosecond), and scalability to sub-20 nm footprints. It covers key material innovations and the role of spintronic effects in enabling compact, energy-efficient neuromorphic systems, providing a foundation for advancing scalable, next-generation computing architectures.