Publications

This is our catalog of publications authored by IBM researchers, in collaboration with the global research community. It’s an ever-growing body of work that shows why IBM is one of the most important contributors to modern computing.

Date

Start

End

Featured collections

Popular topics

29 results for Jungwook Choi

RaPiD: AI Accelerator for Ultra-Low Precision Training and Inference
- - Swagath Venkataramani
  - Vijayalakshmi Srinivasan
  - et al.
- 2021
- ISCA 2021
A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling
- - Ankur Agrawal
  - Saekyu Lee
  - et al.
- 2021
- ISSCC 2021
Hybrid 8-bit floating point (HFP8) training and inference for deep neural networks
- - Xiao Sun
  - Jungwook Choi
  - et al.
- 2019
- NeurIPS 2019
BiScaled-DNN: Quantizing long-tailed datastructures with two scale factors for deep neural networks
- - Shubham Jain
  - Swagath Venkataramani
  - et al.
- 2019
- DAC 2019
Accumulation bit-width scaling for ultra-low precision training of deep networks
- - Charbel Sakr
  - Naigang Wang
  - et al.
- 2019
- ICLR 2019
Training deep neural networks with 8-bit floating point numbers
- - Naigang Wang
  - Jungwook Choi
  - et al.
- 2018
- NeurIPS 2018
PROMISE: An end-to-End design of a programmable mixed-Signal accelerator for Machine-Learning algorithms
- - Prakalp Srivastava
  - Mingu Kang
  - et al.
- 2018
- ISCA 2018
Compensated-DNN: Energy efficient low-precision deep neural networks by compensating quantization errors
- - Shubham Jain
  - Swagath Venkataramani
  - et al.
- 2018
- DAC 2018
ADaComP: Adaptive residual gradient compression for data-parallel distributed training
- - Chia-Yu Chen
  - Jungwook Choi
  - et al.
- 2018
- AAAI 2018
INVITED: Accelerator Design for Deep Learning Training: Extended Abstract: Invited
- - Ankur Agrawal
  - Chia-Yu Chen
  - et al.
- 2017
- DAC 2017