Jongse Park

发表

A Network-Centric Hardware/Algorithm Co-Design to Accelerate Distributed Training of Deep Neural Networks

Mohammad Alian, Nam Sung Kim, Alexander G. Schwing, 2018, 2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

Rollback-free value prediction with approximate loads

Onur Mutlu, Todd C. Mowry, Gennady Pekhimenko, 2014, 2014 23rd International Conference on Parallel Architecture and Compilation (PACT).

From High-Level Deep Network Models to FPGA Acceleration

Asit K. Mishra, Jongse Park, Bradley Thwaites, 2016 .

Toward General-Purpose Code Acceleration with Analog Computation

L. Ceze, D. Burger, A. Hassibi, 2014 .

Approximate Program q : quality loss level β : confidence interval S : Success rate Quality Control Knob Training Data Core Classifer statistical optimization Instrument Statistical Optimizer hardware classification Input Datasets Accelerator training Data Collector Classier Training Algorithm Table

H. Esmaeilzadeh, Jongse Park, Bradley Thwaites, 2016 .

Prediction-Based Quality Control for Approximate Accelerators

Hadi Esmaeilzadeh, Jongse Park, Divya Mahajan, 2015 .

AxGames: Towards Crowdsourcing Quality Target Determination in Approximate Computing

Hadi Esmaeilzadeh, Jongse Park, Divya Mahajan, 2016, ASPLOS.

CoVA: Exploiting Compressed-Domain Analysis to Accelerate Video Analytics

Jongse Park, Hardik Sharma, Jinwoo Hwang, 2022, ArXiv.

Towards Statistical Guarantees in Controlling Quality Tradeoffs for Approximate Acceleration

Hadi Esmaeilzadeh, Jongse Park, Divya Mahajan, 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).

Yin-Yang: Programming Abstractions for Cross-Domain Multi-Acceleration

H. Esmaeilzadeh, Jongse Park, J. Kim, 2022, IEEE Micro.

Breaking the abstractions for productivity and performance in the era of specialization

Jongsea Park, Jongse Park, 2018 .

Scale-Out Acceleration for Machine Learning

Hadi Esmaeilzadeh, Jongse Park, Divya Mahajan, 2017, 2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network

Hadi Esmaeilzadeh, Jongse Park, Vikas Chandra, 2017, 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA).

From high-level deep neural models to FPGAs

Asit K. Mishra, Hadi Esmaeilzadeh, Jongse Park, 2016, 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

TABLA: A unified template-based framework for accelerating statistical machine learning

Hadi Esmaeilzadeh, Jongse Park, Divya Mahajan, 2016, 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA).

TNPU: Supporting Trusted Execution with Tree-less Integrity Protection for Neural Processing Unit

Jaehyuk Huh, Jongse Park, Sunho Lee, 2022, 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA).

Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing

Youngjin Kwon, Jaehyuk Huh, Jongse Park, 2022, USENIX Annual Technical Conference.

Common Counters: Compressed Encryption Counters for Secure GPU Memory

Jaehyuk Huh, Jongse Park, Yeonjae Kim, 2021, 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA).

Tunable Memory Protection for Secure Neural Processing Units

Jaehyuk Huh, Jongse Park, Sunho Lee, 2022, ICCD.

General-purpose code acceleration with limited-precision analog computation

Luis Ceze, Doug Burger, Hadi Esmaeilzadeh, 2014, 2014 ACM/IEEE 41st International Symposium on Computer Architecture (ISCA).

FlexJava: language support for safe and modular approximate programming

Xin Zhang, William R. Harris, Mayur Naik, 2015, ESEC/SIGSOFT FSE.

Neural acceleration for GPU throughput processors

Hadi Esmaeilzadeh, Jongse Park, Amir Yazdanbakhsh, 2015, 2015 48th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

ExpAX: A Framework for Automating Approximate Programming

Xin Zhang, Mayur Naik, Kangqi Ni, 2014 .

Multi-model Machine Learning Inference Serving with GPU Spatial Partitioning pdf

Jaehyuk Huh, Youngjin Kwon, Jongse Park, 2021, ArXiv.

FlexBlock: A Flexible DNN Training Accelerator With Multi-Mode Block Floating Point Support

J. Kung, Seunghyun Lee, Jongse Park, 2022, IEEE Transactions on Computers.

Stockade: Hardware Hardening for Distributed Trusted Sandboxes

Youngjin Kwon, Jaehyuk Huh, Jongse Park, 2021, ArXiv.

AxGames: Towards Crowdsourcing Quality Target Determination in Approximate Computing

Hadi Esmaeilzadeh, Jongse Park, Divya Mahajan, 2016, ASPLOS.

Supporting Dynamic Translation Granularity for Hybrid Memory Systems

Chang Hyun Park, Jaehyuk Huh, Jongse Park, 2022, 2022 IEEE 40th International Conference on Computer Design (ICCD).

Towards Statistical Guarantees in Controlling Quality Tradeoffs for Approximate Acceleration

Hadi Esmaeilzadeh, Jongse Park, Divya Mahajan, 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).

Accelerating GPU accelerators through neural algorithmic transformation

Hadi Esmaeilzadeh, Jongse Park, Amir Yazdanbakhsh, 2017 .

Axilog: Language support for approximate hardware design

Kia Bazargan, Hadi Esmaeilzadeh, Jongse Park, 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).

Tandem Processor: Grappling with Emerging Operators in Neural Networks

H. Esmaeilzadeh, Jongse Park, A. Yazdanbakhsh, 2024, ASPLOS.