Jungwook Choi

发表

Approximate computing: Challenges and opportunities

Vijayalakshmi Srinivasan, Zehra Sura, Sunil Shukla, 2016, 2016 IEEE International Conference on Rebooting Computing (ICRC).

Video-Rate Stereo Matching Using Markov Random Field TRW-S Inference on a Hybrid CPU+FPGA Computing Platform

Rob A. Rutenbar, Jungwook Choi, 2016, IEEE Transactions on Circuits and Systems for Video Technology.

Toward a pixel-parallel architecture for graph cuts inference on FPGA

Rob A. Rutenbar, Jungwook Choi, Tianqi Gao, 2017, 2017 27th International Conference on Field Programmable Logic and Applications (FPL).

VLSI for 5000-word continuous speech recognition

Wonyong Sung, Jungwook Choi, Kisun You, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

Exploiting approximate computing for deep learning acceleration

Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, 2018, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).

Training Deep Neural Networks with 8-bit Floating Point Numbers

Daniel Brand, Kailash Gopalakrishnan, Jungwook Choi, 2018, NeurIPS.

Compensated-DNN: Energy Efficient Low-Precision Deep Neural Networks by Compensating Quantization Errors

Swagath Venkataramani, Vijayalakshmi Srinivasan, Shubham Jain, 2018, 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC).

Configurable and scalable belief propagation accelerator for computer vision

Rob A. Rutenbar, Jungwook Choi, Jungwook Choi, 2016, 2016 26th International Conference on Field Programmable Logic and Applications (FPL).

An FPGA implementation of speech recognition with weighted finite state transducers

Wonyong Sung, Jungwook Choi, Kisun You, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Error resilient MRF message passing architecture for stereo matching

Rob A. Rutenbar, Naresh R. Shanbhag, Jungwook Choi, 2013, SiPS 2013 Proceedings.

A numerical investigation of lean operation characteristics of spark ignition gas engine fueled with biogas and added hydrogen under various boost pressures

Jungwook Choi, Jungsoo Park, Jungsoo Park, 2017 .

AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training pdf

Wei Zhang, Daniel Brand, Ankur Agrawal, 2017, AAAI.

OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator

Jungwook Choi, Junki Park, Jae-Joon Kim, 2020, MLSys.

FPGA acceleration of Markov Random Field TRW-S inference for stereo matching

Rob A. Rutenbar, Jungwook Choi, 2013, 2013 Eleventh ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE 2013).

Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN) pdf

Swagath Venkataramani, Zhuo Wang, Vijayalakshmi Srinivasan, 2018, ArXiv.

Video-Rate Stereo Matching Using Markov Random Field TRW-S Inference on a Hybrid CPU+FPGA Computing Platform

Rob A. Rutenbar, Jungwook Choi, Jungwook Choi, 2013, IEEE Transactions on Circuits and Systems for Video Technology.

Hardware implementation of MRF map inference on an FPGA platform

Rob A. Rutenbar, Jungwook Choi, 2012, 22nd International Conference on Field Programmable Logic and Applications (FPL).

Accurate and Efficient 2-bit Quantized Neural Networks

Swagath Venkataramani, Vijayalakshmi Srinivasan, Zhuo Wang, 2019, MLSys.

Taming the beast: Programming Peta-FLOP class Deep Learning Systems

Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, 2018, ISLPED.

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Swagath Venkataramani, Wonyong Sung, Sungho Shin, 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

PACT: Parameterized Clipping Activation for Quantized Neural Networks pdf

Swagath Venkataramani, Zhuo Wang, Vijayalakshmi Srinivasan, 2018, ArXiv.

Analysis of error resiliency of belief propagation in computer vision

Rob A. Rutenbar, Naresh R. Shanbhag, Jungwook Choi, 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Across the Stack Opportunities for Deep Learning Acceleration

Joel Silberman, Swagath Venkataramani, Matthew M. Ziegler, 2018, ISLPED.

BiScaled-DNN: Quantizing Long-tailed Datastructures with Two Scale Factors for Deep Neural Networks

Swagath Venkataramani, Vijayalakshmi Srinivasan, Shubham Jain, 2019, 2019 56th ACM/IEEE Design Automation Conference (DAC).

Invited: Accelerator design for deep learning training

Swagath Venkataramani, Sunil Shukla, Kailash Gopalakrishnan, 2017, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC).

High performance and error resilient probabilistic inference system for machine learning

Jungwook Choi, 2015 .

A Handover Protocol for the IEEE WAVE-based Wireless Networks

Hyukjoon Lee, Young-uk Chung, Yong-Hoon Choi, 2011 .

A Scalable Multi-TeraOPS Core for AI Training and Inference

Swagath Venkataramani, Vijayalakshmi Srinivasan, George Gristede, 2018, IEEE Solid-State Circuits Letters.

Transmission Power Control with the Guaranteed Communication Reliability in WSN

Jinsung Cho, Ben Lee, Dae-Young Kim, 2015, Int. J. Distributed Sens. Networks.

POSTER: Design Space Exploration for Performance Optimization of Deep Neural Networks on Shared Memory Accelerators

Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, 2017, 2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT).

Deformable Carbon Nanotube-Contact Pads for Inertial Microswitch to Extend Contact Time

Jungwook Choi, Jae-Ik Lee, Jongbaeg Kim, 2012, IEEE Transactions on Industrial Electronics.

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

Wei Wang, Swagath Venkataramani, Hiroshi Inoue, 2019, 2019 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS).

Error Resilient and Energy Efficient MRF Message-Passing-Based Stereo Matching

Rob A. Rutenbar, Naresh R. Shanbhag, Jungwook Choi, 2016, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

A Real-Time FPGA-Based 20 000-Word Speech Recognizer With Optimized DRAM Access

Wonyong Sung, Jungwook Choi, Kisun You, 2010, IEEE Transactions on Circuits and Systems I: Regular Papers.

True Gradient-Based Training of Deep Binary Activated Neural Networks Via Continuous Binarization

Charbel Sakr, Naresh R. Shanbhag, Zhuo Wang, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

CH-MAC: A Cluster-based, Hybrid TDMA MAC Protocol over Wireless Ad-hoc Networks

Yung Yi, Jungwook Choi, Jinhwan Jung, 2019, MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM).

A robust message passing based stereo matching kernel via system-level error resiliency

Rob A. Rutenbar, Naresh R. Shanbhag, Jungwook Choi, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Energy-Efficient Simultaneous Localization and Mapping via Compounded Approximate Computing

Kailash Gopalakrishnan, Jungwook Choi, Jinwook Oh, 2016, 2016 IEEE International Workshop on Signal Processing Systems (SiPS).

A Study on the Development of Ubiquitous CellPhone Robot

Dongwook Kim, Seungwoo Kim, Jungwook Choi, 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Flexible and Expandable Speech Recognition Hardware with Weighted Finite State Transducers

Wonyong Sung, Jungwook Choi, Kisun You, 2012, J. Signal Process. Syst..

A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference

Joel Silberman, Swagath Venkataramani, Matthew M. Ziegler, 2018, 2018 IEEE Symposium on VLSI Circuits.

Fast hierarchical implementation of sequential tree-reweighted belief propagation for probabilistic inference

Rob A. Rutenbar, Eriko Nurvitadhi, José F. Martínez, 2015, 2015 25th International Conference on Field Programmable Logic and Applications (FPL).

Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks pdf

Charbel Sakr, Naresh R. Shanbhag, Ankur Agrawal, 2019, ICLR.

Memory and Interconnect Optimizations for Peta-Scale Deep Learning Systems

Swagath Venkataramani, Philip Heidelberger, Vijayalakshmi Srinivasan, 2019, 2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC).

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

Sujan Kumar Gonugondla, Naresh R. Shanbhag, Nam Sung Kim, 2018, 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA).

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

Swagath Venkataramani, Xiao Sun, Mingu Kang, 2020, 2020 IEEE Symposium on VLSI Circuits.

Performance-driven Programming of Multi-TFLOP Deep Learning Accelerators*

Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, 2019, 2019 IEEE International Symposium on Workload Characterization (IISWC).

Approximate Computing Techniques for Deep Neural Networks

Swagath Venkataramani, Jungwook Choi, 2019, Approximate Circuits.

Supporting handover in an IEEE 802.11p-based wireless access system

Hyukjoon Lee, Jungwook Choi, 2010, VANET '10.

A Multi-path QoS Routing Protocol for the OFDM-TDMA Mesh Networks

Hyukjoon Lee, Jungwook Choi, 2015 .

Memory Access Optimized VLSI for 5000-Word Continuous Speech Recognition

Wonyong Sung, Jungwook Choi, Kisun You, 2011, J. Signal Process. Syst..

Optimization of dual-loop exhaust gas recirculation splitting for a light-duty diesel engine with model-based control

Jungwook Choi, Jungsoo Park, Jungsoo Park, 2016 .

Accelerator Design for Deep Learning Training: Extended Abstract: Invited

Wei Zhang, Swagath Venkataramani, Vijayalakshmi Srinivasan, 2017, DAC.