( en | de | ru )

 

Disambiguation of "Brew, Kevin"

Author of the publication

copydeleteadd this publication to your clipboard
Bilayer Ga-Sb Phase Change Memory with Intermediate Resistance State.

, , , , , , , , , , and . DRC, page 1-2. IEEE, (2021)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Alexander Ali Kevin

Siegfried Brewka

Alena Brewka

Wilhelm Brewe

August Brewig

 

Other publications of authors with the same name

Impact of Phase-Change Memory Flicker Noise and Weight Drift on Analog Hardware Inference for Large-Scale Deep Learning Networks., , , , , , , , , and 4 other author(s). Adv. Intell. Syst., (2022)Mushroom-Type phase change memory with projection liner: An array-level demonstration of conductance drift and noise mitigation., , , , , , , , , and 21 other author(s). IRPS, page 1-6. IEEE, (2021)Measurement of onset of structural relaxation in melt-quenched phase change materials., , , , , , , and . CoRR, (2021)HERMES Core - A 14nm CMOS and PCM-based In-Memory Compute Core using an array of 300ps/LSB Linearized CCO-based ADCs and local digital processing., , , , , , , , , and 14 other author(s). VLSI Circuits, page 1-2. IEEE, (2021)Phase Change Memory-based Hardware Accelerators for Deep Neural Networks (invited)., , , , , , , , , and 15 other author(s). VLSI Technology and Circuits, page 1-2. IEEE, (2023)Bilayer Ga-Sb Phase Change Memory with Intermediate Resistance State., , , , , , , , , and 1 other author(s). DRC, page 1-2. IEEE, (2021)In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory., , , , , , , , , and 8 other author(s). CoRR, (2022)Gradient descent-based programming of analog in-memory computing cores., , , , , , , , , and 4 other author(s). CoRR, (2023)Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited)., , , , , , , , , and 11 other author(s). IRPS, page 1-10. IEEE, (2023)Ultrathin high band gap solar cells with improved efficiencies from the world’s oldest photovoltaic material, , , , , , , , and . Nature Communications, 8 (1): 682 (September 2017)