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Found 13 papers, 1 papers with code
GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS
Object detection is widely used on embedded devices.
GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPS
In this paper, we optimize the semantic segmentation model in order to fully utilize the limited memory and the supported operators on the CNN accelerator chips, and at the same time reduce the CPU load of the CNN model to zero.
SuperOCR: A Conversion from Optical Character Recognition to Image Captioning
The existing methods normally detect where the characters are, and then recognize the character for each detected location.
Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device
Recent years NLP research has witnessed the record-breaking accuracy improvement by DNN models.
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications
In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips.
SuperTML: Two-Dimensional Word Embedding and Transfer Learning Using ImageNet Pretrained CNN Models for the Classifications on Tabular Data
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 approach.
SuperCaptioning: Image Captioning Using Two-dimensional Word Embedding
In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model.
SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models
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 approach.
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data
Tabular data is the most commonly used form of data in industry.
Squared English Word: A Method of Generating Glyph to Use Super Characters for Sentiment Analysis
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.
MRAM Co-designed Processing-in-Memory CNN Accelerator for Mobile and IoT Applications
We designed a device for Convolution Neural Network applications with non-volatile MRAM memory and computing-in-memory co-designed architecture.
Super Characters: A Conversion from Sentiment Classification to Image Classification
We propose a method named Super Characters for sentiment classification.
Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applications
Furthermore, in order to better support real-world deployment for various application scenarios, especially with low-end mobile and embedded platforms and MCUs (Microcontroller Units), we also designed algorithms to fully utilize the CNN-DSA accelerator efficiently by reducing the dependency on external accelerator computation resources, including implementation of Fully-Connected (FC) layers within the accelerator and compression of extracted features from the CNN-DSA accelerator.
