RaPiD: AI Accelerator for Ultra-Low Precision Training and Inference
- Swagath Venkataramani
- Vijayalakshmi Srinivasan
- et al.
- 2021
- ISCA 2021
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11 results for Jinwook Oh
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
INVITED: Accelerator Design for Deep Learning Training: Extended Abstract: Invited
- Ankur Agrawal
- Chia-Yu Chen
- et al.
- 2017
- DAC 2017
A 7-nm Four-Core Mixed-Precision AI Chip with 26.2-TFLOPS Hybrid-FP8 Training, 104.9-TOPS INT4 Inference, and Workload-Aware Throttling
- Sae Kyu Lee
- Ankur Agrawal
- et al.
- 2021
- IEEE JSSC
A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference
- Jinwook Oh
- Sae Kyu Lee
- et al.
- 2020
- VLSI Circuits 2020
SC-Conv: Sparse-complementary convolution for efficient model utilization on CNNs
- Chun Fu Chen
- Jinwook Oh
- et al.
- 2018
- ISM 2018
A Scalable Multi-TeraOPS Deep Learning Processor Core for AI Trainina and Inference
- Bruce Fleischer
- Sunil Shukla
- et al.
- 2018
- VLSI Circuits 2018
Energy-efficient simultaneous localization and mapping via compounded approximate computing
- Jinwook Oh
- Jungwook Choi
- et al.
- 2016
- SiPS 2016
Approximate computing: Challenges and opportunities
- Ankur Agrawal
- Jungwook Choi
- et al.
- 2016
- ICRC 2016
Efficient AI System Design with Cross-Layer Approximate Computing
- Swagath Venkataramani
- Xiao Sun
- et al.
- 2020
- Proceedings of the IEEE