| A 201.4 GOPS 496 mW real-time multi-object recognition processor with bio-inspired neural perception engine JY Kim, M Kim, S Lee, J Oh, K Kim, HJ Yoo IEEE Journal of Solid-State Circuits 45 (1), 32-45, 2009 | 146 | 2009 |
| A Scalable Multi-TeraOPS Deep Learning Processor Core for AI Training and Inference B Fleischer, S Shukla, M Ziegler, J Silberman, J Oh, V Srinivasan, J Choi, ... 2018 IEEE Symposium on VLSI Circuits, 35-36, 2018 | 103 | 2018 |
| A 345 mW heterogeneous many-core processor with an intelligent inference engine for robust object recognition S Lee, J Oh, J Park, J Kwon, M Kim, HJ Yoo IEEE Journal of Solid-State Circuits 46 (1), 42-51, 2010 | 100 | 2010 |
| Approximate computing: Challenges and opportunities A Agrawal, J Choi, K Gopalakrishnan, S Gupta, R Nair, JODA Prener, ... Rebooting Computing (ICRC), IEEE International Conference on, 1-8, 2016 | 82 | 2016 |
| A 320 mW 342 GOPS Real-Time Dynamic Object Recognition Processor for HD 720p Video Streams J Oh, G Kim, J Park, I Hong, S Lee, JY Kim, JH Woo, HJ Yoo Solid-State Circuits, IEEE Journal of 48 (1), 33-45, 2013 | 62 | 2013 |
| A 1.22 TOPS and 1.52 mW/MHz augmented reality multicore processor with neural network NoC for HMD applications G Kim, K Lee, Y Kim, S Park, I Hong, K Bong, HJ Yoo IEEE Journal of Solid-State Circuits 50 (1), 113-124, 2014 | 45 | 2014 |
| A 57mW embedded mixed-mode neuro-fuzzy accelerator for intelligent multi-core processor J Oh, J Park, G Kim, S Lee, HJ Yoo 2011 IEEE International Solid-State Circuits Conference, 130-132, 2011 | 26 | 2011 |
| Low-Power Real-Time Object Recognition Processors for Mobile Vision Systems J Oh, G Kim, I Hong, J Park, S Lee, J Kim, J Woo, H Yoo IEEE MICRO 32 (6), 38-50, 2012 | 22 | 2012 |
| A 57 mW 12.5 µJ/Epoch embedded mixed-mode neuro-fuzzy processor for mobile real-time object recognition J Oh, G Kim, BG Nam, HJ Yoo IEEE journal of solid-state circuits 48 (11), 2894-2907, 2013 | 21 | 2013 |
| A 118.4 gb/s multi-casting network-on-chip with hierarchical star-ring combined topology for real-time object recognition JY Kim, J Park, S Lee, M Kim, J Oh, HJ Yoo IEEE Journal of Solid-State Circuits 45 (7), 1399-1409, 2010 | 21 | 2010 |
| A 92-mW Real-Time Traffic Sign Recognition System With Robust Illumination Adaptation and Support Vector Machine J Park, J Kwon, J Oh, S Lee, JY Kim, HJ Yoo Solid-State Circuits, IEEE Journal of 47 (11), 2711-2723, 2012 | 20 | 2012 |
| A 7nm 4-core ai chip with 25.6 tflops hybrid fp8 training, 102.4 tops int4 inference and workload-aware throttling A Agrawal, SK Lee, J Silberman, M Ziegler, M Kang, S Venkataramani, ... 2021 IEEE International Solid-State Circuits Conference (ISSCC) 64, 144-146, 2021 | 19 | 2021 |
| Tightly coupled processor arrays using coarse grained reconfigurable architecture with iteration level commits CY Chen, K Gopalakrishnan, J Oh, SK Shukla, V Srinivasan US Patent App. 10/120,685, 2018 | 17 | 2018 |
| Tightly coupled processor arrays using coarse grained reconfigurable architecture with iteration level commits CY Chen, K Gopalakrishnan, J Oh, SK Shukla, V Srinivasan US Patent App. 14/932,672, 2015 | 17 | 2015 |
| An 86mW 98GOPS ANN-Searching Processor for Full-HD 30fps Video Object Recognition with Zeroless Locality-Sensitive Hashing G Kim, J Oh, S Lee, HJ Yoo Solid-State Circuits, IEEE Journal of 48 (7), 1615-1624, 2013 | 17 | 2013 |
| 1.2-mW Online Learning Mixed-Mode Intelligent Inference Engine for Low-Power Real-Time Object Recognition Processor J Oh, S Lee, HJ Yoo Very Large Scale Integration (VLSI) Systems, IEEE Transactions on 21 (5 …, 2013 | 17 | 2013 |
| A 3.0 tflops 0.62 v scalable processor core for high compute utilization ai training and inference J Oh, SK Lee, M Kang, M Ziegler, J Silberman, A Agrawal, ... 2020 IEEE Symposium on VLSI Circuits, 1-2, 2020 | 16 | 2020 |
| A 1.2 mW on-line learning mixed mode intelligent inference engine for robust object recognition J Oh, S Lee, M Kim, J Kwon, J Park, JY Kim, HJ Yoo 2010 Symposium on VLSI Circuits, 17-18, 2010 | 16 | 2010 |
| Real-time object recognition with neuro-fuzzy controlled workload-aware task pipelining JY Kim, M Kim, S Lee, J Oh, S Oh, HJ Yoo IEEE micro 29 (6), 28-43, 2009 | 15 | 2009 |
| A scalable multi-TeraOPS core for AI training and inference S Shukla, B Fleischer, M Ziegler, J Silberman, J Oh, V Srinivasan, J Choi, ... IEEE Solid-State Circuits Letters 1 (12), 217-220, 2018 | 14 | 2018 |
Junyoung ParkKAISTVerified email at kaist.ac.kr
