• 200 Citations
  • 9 h-Index
20092019
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Fingerprint Dive into the research topics where Jungwook Choi is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Field programmable gate arrays (FPGA) Engineering & Materials Science
Particle accelerators Engineering & Materials Science
Hardware Engineering & Materials Science
Message passing Engineering & Materials Science
Speech recognition Engineering & Materials Science
Continuous speech recognition Engineering & Materials Science
Data storage equipment Engineering & Materials Science
Accelerator Mathematics

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Research Output 2009 2019

  • 200 Citations
  • 9 h-Index
  • 25 Conference contribution
  • 6 Article
  • 1 Paper
  • 1 Conference article

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. & Gopalakrishnan, K., 2019 Jan 1.

Research output: Contribution to conferencePaper

scaling
Optimal systems
Chemical activation
Hardware
floating
1 Citation (Scopus)

A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation Kernel

Ogawa, E., Ishizaki, K., Inoue, H., Venkataramani, S., Choi, J., Wang, W., Srinivasan, V., Ohara, M. & Gopalakrishnan, K., 2019 May 23, IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 8721357. (IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2019 - Proceedings).

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

Particle accelerators
Data transfer
Computer hardware
Energy efficiency
Neural networks

BiScaled-DNN: Quantizing long-tailed datastructures with two scale factors for deep neural networks

Jain, S., Venkataramani, S., Srinivasan, V., Choi, J., Gopalakrishnan, K. & Chang, L., 2019 Jun 2, Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers Inc., a201. (Proceedings - Design Automation Conference).

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

Scale factor
Data Structures
Neural Networks
Fixed point
Range of data

DeepTools: Compiler and Execution Runtime Extensions for RaPiD AI Accelerator

Venkataramani, S., Choi, J., Srinivasan, V., Wang, W., Zhang, J., Schaal, M., Serrano, M. J., Ishizaki, K., Inoue, H., Ogawa, E., Ohara, M., Chang, L. & Gopalakrishnan, K., 2019 Sep 1, In : IEEE Micro. 39, 5, p. 102-111 10 p., 8782645.

Research output: Contribution to journalArticle

Particle accelerators
Data transfer
Processing
Deep learning

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Shin, S., Jo, Y., Choi, J., Venkataramani, S., Srinivasan, V. & Sung, W., 2019 May 1, 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 1453-1457 5 p. 8683053. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. 2019-May).

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

Artificial intelligence
Energy efficiency
Throughput
Graphics processing unit
Deep neural networks