Fingerprint The fingerprint is based on mining the text of the scientific documents related to the associated persons. Based on that an index of weighted terms is created, which defines the key subjects of research unit

Data storage equipment Engineering & Materials Science
Planning Engineering & Materials Science
Experiments Engineering & Materials Science
Scheduling Engineering & Materials Science
Students Engineering & Materials Science
Semantics Engineering & Materials Science
Costs Engineering & Materials Science
Communication Engineering & Materials Science

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

Research Output 1982 2019

24.5 A Twin-8T SRAM Computation-In-Memory Macro for Multiple-Bit CNN-Based Machine Learning

Si, X., Chen, J. J., Tu, Y. N., Huang, W. H., Wang, J. H., Chiu, Y. C., Wei, W. C., Wu, S. Y., Sun, X., Liu, R., Yu, S., Liu, R. S., Hsieh, C. C., Tang, K. T., Li, Q. & Chang, M. F., Mar 6 2019, 2019 IEEE International Solid-State Circuits Conference, ISSCC 2019. Institute of Electrical and Electronics Engineers Inc., p. 396-398 3 p. 8662392. (Digest of Technical Papers - IEEE International Solid-State Circuits Conference; vol. 2019-February).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Static random access storage
Macros
Learning systems
Data storage equipment
Energy efficiency

25.2 A Reconfigurable RRAM Physically Unclonable Function Utilizing Post-Process Randomness Source with <6×10 -6 Native Bit Error Rate

Pang, Y., Gao, B., Wu, D., Yi, S., Liu, Q., Chen, W. H., Chang, T. W., Lin, W. E., Sun, X., Yu, S., Qian, H., Chang, M. F. & Wu, H., Mar 6 2019, 2019 IEEE International Solid-State Circuits Conference, ISSCC 2019. Institute of Electrical and Electronics Engineers Inc., p. 402-404 3 p. 8662307. (Digest of Technical Papers - IEEE International Solid-State Circuits Conference; vol. 2019-February).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Bit error rate
Silicon
Authentication
Cryptography
RRAM

Accurate patient-specific machine learning models of glioblastoma invasion using transfer learning

Hu, L. S., Yoon, H., Eschbacher, J. M., Baxter, L. C., Dueck, A. C., Nespodzany, A., Smith, K. A., Nakaji, P., Xu, Y., Wang, L., Karis, J. P., Hawkins-Daarud, A. J., Singleton, K. W., Jackson, P. R., Anderies, B. J., Bendok, B. R., Zimmerman, R. S., Quarles, C., Porter-Umphrey, A. B., Mrugala, M. M. & 11 othersSharma, A., Hoxworth, J. M., Sattur, M. G., Sanai, N., Koulemberis, P. E., Krishna, C., Mitchell, J. R., Wu, T., Tran, N. L., Swanson, K. R. & Li, J., Jan 1 2019, In : American Journal of Neuroradiology. 40, 3, p. 418-425 8 p.

Research output: Contribution to journalArticle

Open Access
Glioblastoma
Cell Count
Neoplasms
Image-Guided Biopsy
Diffusion Tensor Imaging