23 citations · 71 across the 12 of their papers we have counts for
12 papers
GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS
Baohua Sun, Tao Zhang, Jiapeng Su +1
Object detection is widely used on embedded devices. With the wide availability of CNN (Convolutional Neural Networks) accelerator chips, the object detection applications are expe…
GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPS
Baohua Sun, Weixiong Lin, Hao Sha +1
Semantic segmentation is the task to cluster pixels on an image belonging to the same class. It is widely used in the real-world applications including autonomous driving, medical…
SuperOCR: A Conversion from Optical Character Recognition to Image Captioning
Baohua Sun, Michael Lin, Hao Sha +1
Optical Character Recognition (OCR) has many real world applications. The existing methods normally detect where the characters are, and then recognize the character for each detec…
Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device
Baohua Sun, Lin Yang, Hao Sha +1
Recent years NLP research has witnessed the record-breaking accuracy improvement by DNN models. However, power consumption is one of the practical concerns for deploying NLP system…
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications
Baohua Sun, Lin Yang, Michael Lin +4
Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use in mobile devices. These chips are mainly used in computer vision applications.…
SuperCaptioning: Image Captioning Using Two-dimensional Word Embedding
Baohua Sun, Lin Yang, Michael Lin +4
Language and vision are processed as two different modal in current work for image captioning. However, recent work on Super Characters method shows the effectiveness of two-dimens…
SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models
Baohua Sun, Lin Yang, Michael Lin +4
The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new a…
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data
Baohua Sun, Lin Yang, Wenhan Zhang +4
Tabular data is the most commonly used form of data in industry. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for clas…
Squared English Word: A Method of Generating Glyph to Use Super Characters for Sentiment Analysis
Baohua Sun, Lin Yang, Catherine Chi +2
The Super Characters method addresses sentiment analysis problems by first converting the input text into images and then applying 2D-CNN models to classify the sentiment. It achie…
MRAM Co-designed Processing-in-Memory CNN Accelerator for Mobile and IoT Applications
Baohua Sun, Daniel Liu, Leo Yu +4
We designed a device for Convolution Neural Network applications with non-volatile MRAM memory and computing-in-memory co-designed architecture. It has been successfully fabricated…
Super Characters: A Conversion from Sentiment Classification to Image Classification
Baohua Sun, Lin Yang, Patrick Dong +3
We propose a method named Super Characters for sentiment classification. This method converts the sentiment classification problem into image classification problem by projecting t…
Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applications
Baohua Sun, Lin Yang, Patrick Dong +3
Computer vision performances have been significantly improved in recent years by Convolutional Neural Networks(CNN). Currently, applications using CNN algorithms are deployed mainl…