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Personal profile

Education/Academic qualification

PHD, University of Maryland-College Park

… → 1990

MS, University of Maryland-College Park

… → 1986

BT, Indian Institute of Technology Kharagpur

… → 1984

Fingerprint Dive into the research topics where Chaitali Chakrabarti is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 4 Similar Profiles
Data storage equipment Engineering & Materials Science
Field programmable gate arrays (FPGA) Engineering & Materials Science
Hardware Engineering & Materials Science
Electric power utilization Engineering & Materials Science
Energy utilization Engineering & Materials Science
Embedded systems Engineering & Materials Science
Throughput Engineering & Materials Science
Scheduling algorithms Engineering & Materials Science

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

Research Output 1989 2019

  • 4226 Citations
  • 32 h-Index
  • 175 Conference contribution
  • 93 Article
  • 8 Chapter
  • 2 Editorial
1 Citation (Scopus)

A Deep Q-Learning Approach for Dynamic Management of Heterogeneous Processors

Gupta, U., Mandal, S. K., Mao, M., Chakrabarti, C. & Ogras, U., Jan 1 2019, (Accepted/In press) In : IEEE Computer Architecture Letters.

Research output: Contribution to journalArticle

Reinforcement learning
Electric power utilization
Experiments
System-on-chip

Joint Optimization of Quantization and Structured Sparsity for Compressed Deep Neural Networks

Srivastava, G., Kadetotad, D., Yin, S., Berisha, V., Chakrabarti, C. & Seo, J., May 1 2019, 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 1393-1397 5 p. 8682791. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. 2019-May).

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

Data storage equipment
Deep neural networks
Degradation

MAX2: ReRAM-Based Neural Network Accelerator That Maximizes Data Reuse and Area Utilization

Mao, M., Peng, X., Liu, R., Li, J., Yu, S. & Chakrabarti, C., Jun 1 2019, In : IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 9, 2, p. 398-410 13 p., 8680623.

Research output: Contribution to journalArticle

Particle accelerators
Tile
Neural networks
Data storage equipment
Processing

Tetris: A streaming accelerator for physics-limited 3D plane-wave ultrasound imaging

West, B. L., Zhou, J., Dreslinski, R. G., Fowlkes, J. B., Kripfgans, O., Chakrabarti, C. & Wenisch, T. F., Jun 2 2019, Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers Inc., a189. (Proceedings - Design Automation Conference).

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

Acoustic streaming
Ultrasound
Accelerator
Streaming
Plane Wave
1 Citation (Scopus)

Algorithm and hardware design of discrete-time spiking neural networks based on back propagation with binary activations

Yin, S., Venkataramanaiah, S. K., Chen, G. K., Krishnamurthy, R., Cao, Y., Chakrabarti, C. & Seo, J., Mar 23 2018, 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., Vol. 2018-January. p. 1-4 4 p.

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

spiking
Backpropagation
hardware
Chemical activation
activation

Projects 1994 2022