Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Web Service Platform for Optimal Quantization of CNN Models

CNN 모델의 최적 양자화를 위한 웹 서비스 플랫폼

  • Roh, Jaewon (Division of Computer Engineering, Hankuk University of Foreign Studies) ;
  • Lim, Chaemin (Division of Computer Engineering, Hankuk University of Foreign Studies) ;
  • Cho, Sang-Young (Division of Computer Engineering, Hankuk University of Foreign Studies)
  • 노재원 (한국외국어대학교 컴퓨터공학부) ;
  • 임채민 (한국외국어대학교 컴퓨터공학부) ;
  • 조상영 (한국외국어대학교 컴퓨터공학부)
  • Received : 2021.12.04
  • Accepted : 2021.12.14
  • Published : 2021.12.31
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Abstract

Low-end IoT devices do not have enough computation and memory resources for DNN learning and inference. Integer quantization of real-type neural network models can reduce model size, hardware computational burden, and power consumption. This paper describes the design and implementation of a web-based quantization platform for CNN deep learning accelerator chips. In the web service platform, we implemented visualization of the model through a convenient UI, analysis of each step of inference, and detailed editing of the model. Additionally, a data augmentation function and a management function of files that store models and inference intermediate results are provided. The implemented functions were verified using three YOLO models.

Keywords

Acknowledgement

본 논문은 과학기술정통신부 및 정보통신기획평가원의 SW 중심대학지원사업의 연구결과로 수행되었음(2019-0-01816). 본 논문은 2021학년도 한국외국어대학교 교내학술연구비 지원에 의하여 이루어진 것임.

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