Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

  • 16 Similar Profiles
Data storage equipment Engineering & Materials Science
RRAM Engineering & Materials Science
Oxides Engineering & Materials Science
Metals Engineering & Materials Science
Electric potential Engineering & Materials Science
Networks (circuits) Engineering & Materials Science
Electrodes Engineering & Materials Science
random access memory Physics & Astronomy

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2008 2017

  • 3725 Citations
  • 28 h-Index
  • 75 Conference contribution
  • 69 Article
  • 2 Chapter
  • 2 Patent
1 Citations
Multilayers
Data storage equipment
Resistors
Energy efficiency
Recovery
1 Citations

Analysis of RRAM Reliability Soft-Errors on the Performance of RRAM-Based Neuromorphic Systems

Tosson, A. M. S., Yu, S., Anis, M. H. & Wei, L. Jul 20 2017 Proceedings - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017. IEEE Computer Society, Vol. 2017-July, p. 62-67 6 p. 7987496

Research output: ResearchConference contribution

RRAM
SPICE
Oxygen vacancies
Throughput
Neural networks

Analyzing inference robustness of RRAM synaptic array in low-precision neural network

Liu, R., Lee, H. Y. & Yu, S. Oct 12 2017 2017 47th European Solid-State Device Research Conference, ESSDERC 2017. Editions Frontieres, p. 18-21 4 p. 8066581

Research output: ResearchConference contribution

Neural networks
RRAM
Resistors
Transistors
Electric potential

A Study of the Effect of RRAM Reliability Soft Errors on the Performance of RRAM-Based Neuromorphic Systems

Tosson, A. M. S., Yu, S., Anis, M. H. & Wei, L. Aug 15 2017 (Accepted/In press) In : IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

Research output: Research - peer-reviewArticle

RRAM
Oxygen vacancies
Neurons
Energy utilization
Data storage equipment
4 Citations

Binary neural network with 16 Mb RRAM macro chip for classification and online training

Yu, S., Li, Z., Chen, P. Y., Wu, H., Gao, B., Wang, D., Wu, W. & Qian, H. Jan 31 2017 2016 IEEE International Electron Devices Meeting, IEDM 2016. Institute of Electrical and Electronics Engineers Inc., p. 16.2.1-16.2.4 7838429

Research output: ResearchConference contribution

education
chips
Macros
Neural networks
RRAM

Projects 2013 2021