A 34-FPS 698-GOP/s/W Binarized Deep Neural Network-based Natural Scene Text Interpretation Accelerator for Mobile Edge Computing

Yixing Li, Zichuan Liu, Wenye Liu, Yu Jiang, Yongliang Wang, Wang Ling Goh, Hao Yu, Fengbo Ren

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

The scene text interpretation is a critical part of natural scene interpretation. Currently, most of the existing work is based on high-end GPU implementation, which is commonly used on the server side. However, in IoT application scenarios, the communication overhead from edge device to the server is quite large, which sometimes even dominates the total processing time. Hence, the edge-computing oriented design is needed to solve this problem. In this paper, we present an architectural design and implementation of a natural scene text interpretation (NSTI) accelerator, which can classify and localize the text region on pixel-level efficiently in real-time on mobile devices. To target the real-time and low-latency processing, the Binary Convolutional Encoder-decoder Network (B-CEDNet) is adopted as the core architecture to enable massive parallelism due to its binary feature. Massively parallelized computations and a highly pipelined data flow control enhance its latency and throughput performance. The NSTI accelerator is implemented in a 40nm CMOS technology, which can process scene text images (size of 128x32) at 34 fps and latency of 40 ms for pixelwise interpretation with the pixelwise classification accuracy over 90% on ICDAR-03 and -13 dataset. The real energy-efficiency is 698 GOP/s/W and the peak energy-efficiency can get up to 7825 GOP/s/W. The proposed accelerator is 7× more energy efficient than its optimized GPU-based implementation counterpart, while maintaining a real-time throughput with latency of 40 ms.

Original languageEnglish (US)
JournalIEEE Transactions on Industrial Electronics
DOIs
StateAccepted/In press - Jan 1 2018

Keywords

  • Application specific integrated circuits
  • Mobile applications
  • Neural network hardware
  • Real-time systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A 34-FPS 698-GOP/s/W Binarized Deep Neural Network-based Natural Scene Text Interpretation Accelerator for Mobile Edge Computing'. Together they form a unique fingerprint.

  • Cite this