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

  • 3 Similar Profiles
Equalizers Engineering & Materials Science
Energy dissipation Engineering & Materials Science
Error compensation Engineering & Materials Science
Electric potential Engineering & Materials Science
Networks (circuits) Engineering & Materials Science
Energy efficiency Engineering & Materials Science
Decoding Engineering & Materials Science
Throughput Engineering & Materials Science

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Research Output 1988 2019

Accumulation bit-width scaling for ultra-low precision training of deep networks

Sakr, C., Wang, N., Chen, C. Y., Choi, J., Agrawal, A., Shanbhag, N. R. & Gopalakrishnan, K., Jan 1 2019.

Research output: Contribution to conferencePaper

scaling
Optimal systems
Chemical activation
Hardware
floating

An energy-efficient classifier via boosted spin channel networks

Patil, A. D., Manipatruni, S., Nikonov, D., Young, I. A. & Shanbhag, N. R., Jan 1 2019, 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 8702648. (Proceedings - IEEE International Symposium on Circuits and Systems; vol. 2019-May).

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

Classifiers
Adaptive boosting
Learning algorithms
Throughput
Networks (circuits)

An Energy-Efficient Programmable Mixed-Signal Accelerator for Machine Learning Algorithms

Kang, M., Srivastava, P., Adve, V. S., Kim, N. S. & Shanbhag, N. R., Sep 1 2019, In : IEEE Micro. 39, 5, p. 64-72 9 p., 8768342.

Research output: Contribution to journalArticle

High level languages
Learning algorithms
Particle accelerators
Energy efficiency
Learning systems

An MRAM-based deep in-memory architecture for deep neural networks

Patil, A. D., Hua, H., Gonugondla, S., Kang, M. & Shanbhag, N. R., Jan 1 2019, 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 8702206. (Proceedings - IEEE International Symposium on Circuits and Systems; vol. 2019-May).

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

Memory architecture
Networks (circuits)
Deep neural networks

Binodal, wireless epidermal electronic systems with in-sensor analytics for neonatal intensive care

Chung, H. U., Kim, B. H., Lee, J. Y., Lee, J., Xie, Z., Ibler, E. M., Lee, K. H., Banks, A., Jeong, J. Y., Kim, J., Ogle, C., Grande, D., Yu, Y., Jang, H., Assem, P., Ryu, D., Kwak, J. W., Namkoong, M., Park, J. B., Lee, Y. & 25 others, Kim, D. H., Ryu, A., Jeong, J., You, K., Ji, B., Liu, Z., Huo, Q., Feng, X., Deng, Y., Xu, Y., Jang, K. I., Kim, J., Zhang, Y., Ghaffari, R., Rand, C. M., Schau, M., Hamvas, A., Weese-Mayer, D. E., Huang, Y., Lee, S. M., Lee, C. H., Shanbhag, N. R., Paller, A. S., Xu, S. & Rogers, J. A., Jan 1 2019, In : Science. 363, 6430

Research output: Contribution to journalArticle

Open Access
Neonatal Intensive Care
Neonatal Intensive Care Units
Skin
Vital Signs
Diagnostic Imaging