• 2861 Citations
  • 20 h-Index
20022020
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Fingerprint Dive into the research topics where Bipin Rajendran is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 4 Similar Profiles
Phase change memory Engineering & Materials Science
Data storage equipment Engineering & Materials Science
Neurons Engineering & Materials Science
Neural networks Engineering & Materials Science
Networks (circuits) Engineering & Materials Science
Brain Engineering & Materials Science
Plasticity Engineering & Materials Science
Learning systems Engineering & Materials Science

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

Projects 2017 2020

Neural networks
Hardware
Deep neural networks
Information theory
Learning algorithms

Research Output 2002 2019

Building next-generation AI systems: Co-optimization of algorithms, architectures, and nanoscale memristive devices

Rajendran, B., Sebastian, A. & Eleftheriou, E., May 1 2019, 2019 IEEE 11th International Memory Workshop, IMW 2019. Institute of Electrical and Electronics Engineers Inc., 8739740. (2019 IEEE 11th International Memory Workshop, IMW 2019).

New Jersey Institute of Technology

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

Brain
Artificial intelligence
Engines

Computational memory-based inference and training of deep neural networks

Sebastian, A., Boybat, I., Dazzi, M., Giannopoulos, I., Jonnalagadda, V., Joshi, V., Karunaratne, G., Kersting, B., Khaddam-Aljameh, R., Nandakumar, S. R., Petropoulos, A., Piveteau, C., Antonakopoulos, T., Rajendran, B., Gallo, M. L. & Eleftheriou, E., Jun 2019, 2019 Symposium on VLSI Circuits, VLSI Circuits 2019 - Digest of Technical Papers. Institute of Electrical and Electronics Engineers Inc., p. T168-T169 8778178. (IEEE Symposium on VLSI Circuits, Digest of Technical Papers; vol. 2019-June).

New Jersey Institute of Technology

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

Data storage equipment
Phase change memory
Artificial intelligence
Learning systems
Deep neural networks

Computational memory-based inference and training of deep neural networks

Sebastian, A., Boybat, I., Dazzi, M., Giannopoulos, I., Jonnalagadda, V., Joshi, V., Karunaratne, G., Kersting, B., Khaddam-Aljameh, R., Nandakumar, S. R., Petropoulos, A., Piveteau, C., Antonakopoulos, T., Rajendran, B., Gallo, M. L. & Eleftheriou, E., Jun 1 2019, 2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers. Institute of Electrical and Electronics Engineers Inc., p. T168-T169 8776518. (Digest of Technical Papers - Symposium on VLSI Technology; vol. 2019-June).

New Jersey Institute of Technology

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

Data storage equipment
Phase change memory
Artificial intelligence
Learning systems
Deep neural networks

Impact of conductance drift on multi-PCM synaptic architectures

Boybat, I., Nandakumar, S. R., Le Gallo, M., Rajendran, B., Leblebici, Y., Sebastian, A. & Eleftheriou, E., Jan 4 2019, NVMTS 2018 - Non-Volatile Memory Technology Symposium 2018. Institute of Electrical and Electronics Engineers Inc., 8603100. (NVMTS 2018 - Non-Volatile Memory Technology Symposium 2018).

New Jersey Institute of Technology

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

Phase change memory
Memory architecture
Data storage equipment

Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients

Rosenfeld, B., Simeone, O. & Rajendran, B., Jul 2019, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019. Institute of Electrical and Electronics Engineers Inc., 8815546. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; vol. 2019-July).

New Jersey Institute of Technology

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

Neural networks
Neurons
Energy utilization
Reinforcement learning
Mobile devices