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349 results on '"JUNGWOOK CHOI"'

51. RaPiD: AI Accelerator for Ultra-low Precision Training and Inference.

Author: Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Wang 0333, Sanchari Sen, Jintao Zhang, Ankur Agrawal, Monodeep Kar, Shubham Jain, Alberto Mannari, Hoang Tran, Yulong Li, Eri Ogawa, Kazuaki Ishizaki, Hiroshi Inoue, Marcel Schaal, Mauricio J. Serrano, Jungwook Choi, Xiao Sun 0013, Naigang Wang, Chia-Yu Chen, Allison Allain, James Bonanno, Nianzheng Cao, Robert Casatuta, Matthew Cohen, Bruce M. Fleischer, Michael Guillorn, Howard Haynie, Jinwook Jung, Mingu Kang, Kyu-Hyoun Kim, Siyu Koswatta, Sae Kyu Lee, Martin Lutz, Silvia M. Mueller, Jinwook Oh, Ashish Ranjan 0001, Zhibin Ren, Scot Rider, Kerstin Schelm, Michael Scheuermann, Joel Silberman, Jie Yang, Vidhi Zalani, Xin Zhang 0025, Ching Zhou, Matthew M. Ziegler, Vinay Shah, Moriyoshi Ohara, Pong-Fei Lu, Brian W. Curran, Sunil Shukla, Leland Chang, and Kailash Gopalakrishnan
Published: 2021
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52. TernGEMM: GEneral Matrix Multiply Library with Ternary Weights for Fast DNN Inference.

Author: Seokhyeon Choi, Kyuhong Shim, Jungwook Choi, Wonyong Sung, and Byonghyo Shim
Published: 2021
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53. Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks.

Author: Yoonho Boo, Sungho Shin, Jungwook Choi, and Wonyong Sung
Published: 2021
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54. Understanding the Role of Self Attention for Efficient Speech Recognition.

Author: Kyuhong Shim, Jungwook Choi, and Wonyong Sung
Published: 2022

55. A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling.

Author: Ankur Agrawal, Sae Kyu Lee, Joel Silberman, Matthew M. Ziegler, Mingu Kang, Swagath Venkataramani, Nianzheng Cao, Bruce M. Fleischer, Michael Guillorn, Matt Cohen, Silvia M. Mueller, Jinwook Oh, Martin Lutz, Jinwook Jung, Siyu Koswatta, Ching Zhou, Vidhi Zalani, James Bonanno, Robert Casatuta, Chia-Yu Chen, Jungwook Choi, Howard Haynie, Alyssa Herbert, Radhika Jain, Monodeep Kar, Kyu-Hyoun Kim, Yulong Li, Zhibin Ren, Scot Rider, Marcel Schaal, Kerstin Schelm, Michael Scheuermann, Xiao Sun 0013, Hung Tran, Naigang Wang, Wei Wang 0333, Xin Zhang 0025, Vinay Shah, Brian W. Curran, Vijayalakshmi Srinivasan, Pong-Fei Lu, Sunil Shukla, Leland Chang, and Kailash Gopalakrishnan
Published: 2021
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56. Implementation of Embedded Testbeds Using USRP and GNU-Radio for Performance Measurement and Analysis of PPS and PCO-Based Time Synchronizations.

Author: Hyun Jae Park, Cheol-woong Lee, Taeyoung Shin, Byeong-Hee Roh, Soo Bum Park, and Jungwook Choi
Published: 2021
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57. Learning from distinctive candidates to optimize reduced-precision convolution program on tensor cores.

Author: Junkyeong Choi, Hyucksung Kwon, Woongkyu Lee, Jungwook Choi, and Jieun Lim 0001
Published: 2022

58. Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization.

Author: Seongmin Park 0003, Beomseok Kwon, Jieun Lim 0001, Kyuyoung Sim, Taeho Kim, and Jungwook Choi
Published: 2022
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59. Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks.

Author: Xiao Sun 0013, Jungwook Choi, Chia-Yu Chen, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Xiaodong Cui, Wei Zhang 0022, and Kailash Gopalakrishnan
Published: 2019

60. Performance-driven Programming of Multi-TFLOP Deep Learning Accelerators.

Author: Swagath Venkataramani, Jungwook Choi, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, and Leland Chang
Published: 2019
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61. Workload-aware Automatic Parallelization for Multi-GPU DNN Training.

Author: Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, and Wonyong Sung
Published: 2019
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62. Memory and Interconnect Optimizations for Peta-Scale Deep Learning Systems.

Author: Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Philip Heidelberger, Leland Chang, and Kailash Gopalakrishnan
Published: 2019
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63. CH-MAC: A Cluster-based, Hybrid TDMA MAC Protocol over Wireless Ad-hoc Networks.

Author: Hoyong Choi, Jihwan Bang, Namjo Ahn, Jinhwan Jung, Jungwook Choi, Soobum Park, and Yung Yi
Published: 2019
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64. DLFloat: A 16-b Floating Point Format Designed for Deep Learning Training and Inference.

Author: Ankur Agrawal, Bruce M. Fleischer, Silvia M. Mueller, Xiao Sun 0013, Naigang Wang, Jungwook Choi, and Kailash Gopalakrishnan
Published: 2019
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65. Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead.

Author: Muhammad Shafique 0001, Mahum Naseer, Theocharis Theocharides, Christos Kyrkou, Onur Mutlu, Lois Orosa 0001, and Jungwook Choi
Published: 2020
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66. Efficient AI System Design With Cross-Layer Approximate Computing.

Author: Swagath Venkataramani, Xiao Sun 0013, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Mingu Kang, Ankur Agarwal, Jinwook Oh, Shubham Jain, Tina Babinsky, Nianzheng Cao, Thomas W. Fox, Bruce M. Fleischer, George Gristede, Michael Guillorn, Howard Haynie, Hiroshi Inoue, Kazuaki Ishizaki, Michael J. Klaiber, Shih-Hsien Lo, Gary W. Maier, Silvia M. Mueller, Michael Scheuermann, Eri Ogawa, Marcel Schaal, Mauricio J. Serrano, Joel Silberman, Christos Vezyrtzis, Wei Wang 0333, Fanchieh Yee, Jintao Zhang, Matthew M. Ziegler, Ching Zhou, Moriyoshi Ohara, Pong-Fei Lu, Brian W. Curran, Sunil Shukla, Vijayalakshmi Srinivasan, Leland Chang, and Kailash Gopalakrishnan
Published: 2020
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67. Hardware and Software Co-optimization for the Initialization Failure of the ReRAM-based Cross-bar Array.

Author: Youngseok Kim, Seyoung Kim, Chun-Chen Yeh, Vijay Narayanan, and Jungwook Choi
Published: 2020
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68. Training Deep Neural Networks with 8-bit Floating Point Numbers.

Author: Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, and Kailash Gopalakrishnan
Published: 2018

69. PROMISE: An End-to-End Design of a Programmable Mixed-Signal Accelerator for Machine-Learning Algorithms.

Author: Prakalp Srivastava, Mingu Kang, Sujan K. Gonugondla, Sungmin Lim, Jungwook Choi, Vikram S. Adve, Nam Sung Kim, and Naresh R. Shanbhag
Published: 2018
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70. True Gradient-Based Training of Deep Binary Activated Neural Networks Via Continuous Binarization.

Author: Charbel Sakr, Jungwook Choi, Zhuo Wang, Kailash Gopalakrishnan, and Naresh R. Shanbhag
Published: 2018
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71. Exploiting approximate computing for deep learning acceleration.

Author: Chia-Yu Chen, Jungwook Choi, Kailash Gopalakrishnan, Viji Srinivasan, and Swagath Venkataramani
Published: 2018
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72. AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training.

Author: Chia-Yu Chen, Jungwook Choi, Daniel Brand, Ankur Agrawal, Wei Zhang, and Kailash Gopalakrishnan
Published: 2018
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73. Compensated-DNN: energy efficient low-precision deep neural networks by compensating quantization errors.

Author: Shubham Jain, Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Pierce Chuang, and Leland Chang
Published: 2018
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74. Layer-wise Pruning of Transformer Attention Heads for Efficient Language Modeling.

Author: Kyuhong Shim, Iksoo Choi, Wonyong Sung, and Jungwook Choi
Published: 2021
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75. Understanding and Reducing Weight-Load Overhead of Systolic Deep Learning Accelerators.

Author: Jinwon Joo, Minyong Yoon, Jungwook Choi, Mingu Kang, Jong-Geon Lee, Jinin So, IlKwon Yun, Yongsuk Kwon, and KyungSoo Kim 0003
Published: 2021
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76. A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation Kernel.

Author: Eri Ogawa, Kazuaki Ishizaki, Hiroshi Inoue, Swagath Venkataramani, Jungwook Choi, Wei Wang 0333, Vijayalakshmi Srinivasan, Moriyoshi Ohara, and Kailash Gopalakrishnan
Published: 2019
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77. OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator.

Author: Junki Park, Hyunsung Yoon, Daehyun Ahn, Jungwook Choi, and Jae-Joon Kim
Published: 2020

78. NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference.

Author: Joonsang Yu, Junki Park, Seongmin Park 0003, Minsoo Kim, Sihwa Lee, Dong Hyun Lee, and Jungwook Choi
Published: 2021

79. Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead.

Author: Muhammad Shafique 0001, Mahum Naseer, Theocharis Theocharides, Christos Kyrkou, Onur Mutlu, Lois Orosa 0001, and Jungwook Choi
Published: 2021

80. DeepTools: Compiler and Execution Runtime Extensions for RaPiD AI Accelerator.

Author: Swagath Venkataramani, Jungwook Choi, Vijayalakshmi Srinivasan, Wei Wang 0333, Jintao Zhang, Marcel Schaal, Mauricio J. Serrano, Kazuaki Ishizaki, Hiroshi Inoue, Eri Ogawa, Moriyoshi Ohara, Leland Chang, and Kailash Gopalakrishnan
Published: 2019
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81. Guest Editorial: Robust Resource-Constrained Systems for Machine Learning.

Author: Theocharis Theocharides, Muhammad Shafique 0001, Jungwook Choi, and Onur Mutlu
Published: 2020
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82. Achieving low write latency through new stealth program operation supporting early write completion in NAND flash memory.

Author: Moonseok Jang, Kexin Wang, Sangjin Lee 0001, Hyeonggyu Jeong, In-Yeong Song, Yong Ho Song, and Jungwook Choi
Published: 2022
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83. Toward a pixel-parallel architecture for graph cuts inference on FPGA.

Author: Tianqi Gao, Jungwook Choi, Shang-nien Tsai, and Rob A. Rutenbar
Published: 2017
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84. Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks.

Author: Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh R. Shanbhag, and Kailash Gopalakrishnan
Published: 2019

85. Accurate and Efficient 2-bit Quantized Neural Networks.

Author: Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, Zhuo Wang, and Pierce Chuang
Published: 2019

86. A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference.

Author: Jinwook Oh, Sae Kyu Lee, Mingu Kang, Matthew M. Ziegler, Joel Silberman, Ankur Agrawal, Swagath Venkataramani, Bruce M. Fleischer, Michael Guillorn, Jungwook Choi, Wei Wang 0333, Silvia M. Mueller, Shimon Ben-Yehuda, James Bonanno, Nianzheng Cao, Robert Casatuta, Chia-Yu Chen, Matt Cohen, Ophir Erez, Thomas W. Fox, George Gristede, Howard Haynie, Vicktoria Ivanov, Siyu Koswatta, Shih-Hsien Lo, Martin Lutz, Gary W. Maier, Alex Mesh, Yevgeny Nustov, Scot Rider, Marcel Schaal, Michael Scheuermann, Xiao Sun 0013, Naigang Wang, Fanchieh Yee, Ching Zhou, Vinay Shah, Brian W. Curran, Vijayalakshmi Srinivasan, Pong-Fei Lu, Sunil Shukla, Kailash Gopalakrishnan, and Leland Chang
Published: 2020
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87. Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks.

Author: Yoonho Boo, Sungho Shin, Jungwook Choi, and Wonyong Sung
Published: 2020

88. Configurable and scalable belief propagation accelerator for computer vision.

Author: Jungwook Choi and Rob A. Rutenbar
Published: 2016
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89. Energy-Efficient Simultaneous Localization and Mapping via Compounded Approximate Computing.

Author: Jinwook Oh, Jungwook Choi, Guilherme C. Januario, and Kailash Gopalakrishnan
Published: 2016
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90. Approximate computing: Challenges and opportunities.

Author: Ankur Agrawal, Jungwook Choi, Kailash Gopalakrishnan, Suyog Gupta, Ravi Nair, Jinwook Oh, Daniel A. Prener, Sunil Shukla, Vijayalakshmi Srinivasan, and Zehra Sura
Published: 2016
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91. Analysis of error resiliency of belief propagation in computer vision.

Author: Jungwook Choi, Ameya D. Patil, Rob A. Rutenbar, and Naresh R. Shanbhag
Published: 2016
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92. Fast hierarchical implementation of sequential tree-reweighted belief propagation for probabilistic inference.

Author: Skand Hurkat, Jungwook Choi, Eriko Nurvitadhi, José F. Martínez, and Rob A. Rutenbar
Published: 2015
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93. Synergistic combination of 2-mercaptobenzothiazole (MBT) and nitrosoamine-safe thiuram disulfide as advanced rubber vulcanizing accelerators

Author: Md Najib Alam, Subhas C Debnath, Orathai Boondamnoen, Koramala N Kumar, Jin H Kim, and Jungwook Choi
Subjects: Polymers and Plastics, Materials Chemistry
Abstract: In this study, we developed a combination accelerator system to synergistically improve the vulcanizing activity of 2-marcapto benzothiazole (MBT) with different nitrosamine-safe thiuram disulfides (TDs), namely, bis-(N-benzyl piperazino) thiuram disulfide (BPTD), bis-(N-phenyl piperazino) thiuram disulfide (PPTD), and bis-(N-ethyl piperazino) thiuram disulfide (EPTD), which can be used as nitrosamine-safe cross-linking accelerators for sulfur based rubber vulcanization. The results were compared with that of a conventionally unsafe TD, tetramethyl thiuram disulfide (TMTD) combined with MBT, to evaluate the efficiencies of these nitrosamine-safe TD accelerators for industrial applications. The curing and mechanical properties of rubber vulcanization were investigated to understand the synergism between MBT and nitrosoamine-safe TDs. The results indicate that novel TDs combined with MBT significantly improve the curing characteristics and mechanical properties following to the TDs with smaller molecular size and higher basicity. The MBT/BPTD system had higher modulus values because BPTD has a higher molar mass, which facilitates a better distribution of accelerators in the rubber matrix. Overall, the MBT/EPTD accelerator systems with equal molar ratios can compete with the curing rates, tensile strengths, and moduli of unsafe TMTD accelerator systems in the vulcanization of rubber.
Published: 2022
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94. Bright red luminescence from Eu3+-activated noncytotoxic SnO2 quantum dots for latent fingerprint detection

Author: K. Naveen Kumar, Rajesh Dagupati, Jiseok Lim, and Jungwook Choi
Subjects: Process Chemistry and Technology, Materials Chemistry, Ceramics and Composites, Surfaces, Coatings and Films, Electronic, Optical and Magnetic Materials
Published: 2022
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