mlsanityData to 7 Oct 2026

GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS

arXiv 2103.15756 ↗First posted 19 Feb 2021cs.AR cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Object detection is widely used on embedded devices. With the wide availability of CNN (Convolutional Neural Networks) accelerator chips, the object detection applications are expected to run with low power consumption, and high inference speed. In addition, the CPU load is expected to be as low as possible for a CNN accelerator chip working as a co-processor with a host CPU. In this paper, we optimize the object detection model on the CNN accelerator chip by minimizing the CPU load. The resulting model is called GnetDet. The experimental result shows that the GnetDet model running on a 224mW chip achieves the speed of 106FPS with excellent accuracy.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 19 Feb 2021.

Builds on

18 of 27 references.

References · 18

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  4. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  5. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  6. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  7. 2018YOLOv3: An Incremental Improvementcited by 2,652
  8. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  9. 2018Super Characters: A Conversion from Sentiment Classification to Image Classificationcited by 9
  10. 2019Squared English Word: A Method of Generating Glyph to Use Super Characters for Sentiment Analysiscited by 6
  11. 2018MRAM Co-designed Processing-in-Memory CNN Accelerator for Mobile and IoT Applicationscited by 5
  12. 2018Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applicationscited by 3
6 more
  1. 2019System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applicationscited by 3
  2. 2020Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Devicecited by 3
  3. 2019SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Modelscited by 2
  4. 2019SuperCaptioning: Image Captioning Using Two-dimensional Word Embeddingcited by 2
  5. 2020SuperOCR: A Conversion from Optical Character Recognition to Image Captioningcited by 2
  6. 2021GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPScited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutionscited by 1