Seungkyu Choi

Orcid: 0000-0002-3125-9707

According to our database1, Seungkyu Choi authored at least 17 papers between 2011 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2023
Accelerating On-Device DNN Training Workloads via Runtime Convergence Monitor.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., May, 2023

Energy-Efficient CNN Personalized Training by Adaptive Data Reformation.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2023

SONA: An Accelerator for Transform-Domain Neural Networks with Sparse-Orthogonal Weights.
Proceedings of the 34th IEEE International Conference on Application-specific Systems, 2023

2022
Rare Computing: Removing Redundant Multiplications From Sparse and Repetitive Data in Deep Neural Networks.
IEEE Trans. Computers, 2022

A Deep Neural Network Training Architecture With Inference-Aware Heterogeneous Data-Type.
IEEE Trans. Computers, 2022

Algorithm/architecture co-design for energy-efficient acceleration of multi-task DNN.
Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022

2021
A Convergence Monitoring Method for DNN Training of On-Device Task Adaptation.
Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2021

2020
An Energy-Efficient Deep Convolutional Neural Network Training Accelerator for In Situ Personalization on Smart Devices.
IEEE J. Solid State Circuits, 2020

A Pragmatic Approach to On-device Incremental Learning System with Selective Weight Updates.
Proceedings of the 57th ACM/IEEE Design Automation Conference, 2020

2019
Compressing Sparse Ternary Weight Convolutional Neural Networks for Efficient Hardware Acceleration.
Proceedings of the 2019 IEEE/ACM International Symposium on Low Power Electronics and Design, 2019

An Optimized Design Technique of Low-bit Neural Network Training for Personalization on IoT Devices.
Proceedings of the 56th Annual Design Automation Conference 2019, 2019

A 47.4µJ/epoch Trainable Deep Convolutional Neural Network Accelerator for In-Situ Personalization on Smart Devices.
Proceedings of the IEEE Asian Solid-State Circuits Conference, 2019

2018
TrainWare: A Memory Optimized Weight Update Architecture for On-Device Convolutional Neural Network Training.
Proceedings of the International Symposium on Low Power Electronics and Design, 2018

2017
Energy-Efficient Design of Processing Element for Convolutional Neural Network.
IEEE Trans. Circuits Syst. II Express Briefs, 2017

SENIN: An energy-efficient sparse neuromorphic system with on-chip learning.
Proceedings of the 2017 IEEE/ACM International Symposium on Low Power Electronics and Design, 2017

2014
Power Allocation Algorithms for GMD or UCD Based Joint Transceiver Designs.
Wirel. Pers. Commun., 2014

2011
A User Scheduling with Minimum-Rate Requirement for Maximum Sum-Rate in MIMO-BC.
IEICE Trans. Commun., 2011


  Loading...