Welcome to Empas lab.

This site is the homepage of EMPAS (Embedded and Parallel Systems) LAB.

If you have any interest in my study, please look at this site and review my publications.


At this time, If you want research collaboration with me, please contact me via email (hyunjin2@dankook.ac.kr).

(I do not allow any situation with one-side advice.)

(*인공지능의 구현과 응용, 양자컴퓨팅과 In-Memory Computing의 주제에 관심있는 학생은

(hyunjin2@dankook.ac.kr)로 연락바랍니다.

Announcements

  • Studies with student researchers (titled as BNNs for sound)

At this time, Prof. Kim and other advised students have studied binarized neural networks (BNNs) from this summer. For 1-D data (e.g. sound), BNNs are a very realistic form to be implemented on lightweight MCUs. Besides, their low latency and low power consumption can extend the applicable area of neural networks.


  • Publication in arXiv - 2022. 06. 26

On June 26, 2022, a manuscript titled as "CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps" has been uploaded in arXiv.


  • A ceremony called Open Lab

On May 23/24, 2022, there is an open lab ceremony to explain the life of graduate schools and method for achieving research contributions. (#403, 2nd Engineering building, Dankook University)


  • ARM Academic Access (AAA) application

Prof. HyunJin Kim had a meeting with ARM to apply the ARM Academic Access (AAA) Program. The program supports ARM commercial IPs, training Program, and Materials for researches with ARM IPs.


  • Acceptance in an International Journal — 2022. 02. 24

A paper titled "A storage-efficient ensemble classification using filter sharing on binarized convolutional neural networks" by HyunJin Kim, Mohammed Alnemari, and Nader Bagherzadeh has been accepted in PeerJ Computer Science.


  • Acceptance in an International Journal — 2021. 12. 14

A paper titled "PresB-Net: parametric binarized neural network with learnable activations and shuffled grouped convolution" by Jungwoo Shin and HyunJin Kim has been accepted in PeerJ Computer Science.


  • Acceptance in a Domestic Journal — 2021. 9. 24

A paper titled "Highly accurate approximate multiplier using heterogeneous inexact 4-2 compressors for error-resilient application" by Jaewoo Lee and HyunJin Kim has been accepted in IEMEK Journal of embedded Systems and Applications (Domestic).


  • Acceptance in an International Journal — 2021. 9. 22

A paper titled "A Cost-Efficient Approximate Dynamic Ranged Multiplication and Approximation-Aware Training on Convolutional Neural Networks" by HyunJin Kim and Alberto A. Del Barrio has been accepted in IEEE Access.


  • Acceptance in an International Journal — 2021. 8. 26

A paper titled "PLAM: a Posit Logarithm-Approximate Multiplier" by Raul Murillo, Alberto A. Del Barrio, Guillermo Botella, Min Soo Kim, HyunJin Kim, Nader Bagherzadeh has been accepted in IEEE Transactions on Emerging Topics in Computing.


  • Support from National Research Foundation — 2021. 6. 1

A project titled "ACDNN: Approximate Computing-based Deep Neural Networks using Inaccurate Arithmetic Units for Low-Power Systems" will be supported by National Research Foundation (NRF) (June 2021 ~ February 2024) - 2021.05.27.


  • Acceptance in an International Journal — 2021. 4. 24

A paper titled “A k-Mismatch String Matching for Generalized Edit Distance using Diagonal Skipping Method” by HyunJin Kim has been accepted in PLOS One.


  • Acceptance in an International Journal — 2021. 3. 2

A paper titled "AresB-Net: accurate residual binarized neural networks using shortcut concatenation and shuffled grouped convolution" by HyunJin Kim has been accepted in PeerJ Computer Science.


  • Acceptance in an International Journal — 2021. 2. 25

A paper titled "A Low-Cost Compensated Approximate Multiplier for Bfloat16 Data Processing on CNN Inference" by HyunJin Kim has been accepted in ETRI Journal.


  • Publication in an international journal — 2021. 1. 14

A Paper titled "Effects of Approximate Multiplication on Convolutional Neural Networks," Kim, M. S., Del Barrio, A. A., Kim, H., & Bagherzadeh, N has been published in the early access of IEEE Transactions on Emerging Topics in Computing.