CIDSE Centers

Fingerprint Dive into the research topics where CIDSE Centers is active. These topic labels come from the works of this organization's members. Together they form a unique fingerprint.

Data storage equipment Engineering & Materials Science

Experiments Engineering & Materials Science

Feature extraction Engineering & Materials Science

Students Engineering & Materials Science

Access control Engineering & Materials Science

Communication Engineering & Materials Science

Semantics Engineering & Materials Science

Labels Engineering & Materials Science

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Profiles

Research Output 1970 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 proceeding › Conference 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 proceeding › Conference 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 others, Sharma, 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 journal › Article

Open Access

Glioblastoma

Cell Count

Neoplasms

Image-Guided Biopsy

Diffusion Tensor Imaging