Lossless Decompression Accelerator for Embedded Processor with GUI

Hwang, Gwan Beom; Cho, Kwon Neung; Oh, Hyun Woo; Yoon, Young Il; Lee, Seung Eun

doi:10.3390/mi12020145

Cited by 6 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The essence of this method is its ability to identify sequential symbols, especially those with the longest match distances, and convert them into length and distance pairs. This allows the algorithm to efficiently compress files by pointing back to previous similar sequences instead of writing out each sequence afresh, leading to considerable data reduction [78,79].…”

Section: Deflate Image Compressionmentioning

confidence: 99%

Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

Shakya,

Vidyarthi

2024

Technologies

View full text Add to dashboard Cite

In response to the COVID-19 pandemic and its strain on healthcare resources, this study presents a comprehensive review of various techniques that can be used to integrate image compression techniques and statistical texture analysis to optimize the storage of Digital Imaging and Communications in Medicine (DICOM) files. In evaluating four predominant image compression algorithms, i.e., discrete cosine transform (DCT), discrete wavelet transform (DWT), the fractal compression algorithm (FCA), and the vector quantization algorithm (VQA), this study focuses on their ability to compress data while preserving essential texture features such as contrast, correlation, angular second moment (ASM), and inverse difference moment (IDM). A pivotal observation concerns the direction-independent Grey Level Co-occurrence Matrix (GLCM) in DICOM analysis, which reveals intriguing variations between two intermediate scans measured with texture characteristics. Performance-wise, the DCT, DWT, FCA, and VQA algorithms achieved minimum compression ratios (CRs) of 27.87, 37.91, 33.26, and 27.39, respectively, with maximum CRs at 34.48, 68.96, 60.60, and 38.74. This study also undertook a statistical analysis of distinct CT chest scans from COVID-19 patients, highlighting evolving texture patterns. Finally, this work underscores the potential of coupling image compression and texture feature quantification for monitoring changes related to human chest conditions, offering a promising avenue for efficient storage and diagnostic assessment of critical medical imaging.

show abstract

Section: Deflate Image Compressionmentioning

confidence: 99%

Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

Shakya,

Vidyarthi

2024

Technologies

View full text Add to dashboard Cite

show abstract

“…The intelligent monitoring device includes an edge AI module. We enabled low-power, high-efficiency computations by constructing systems with dedicated processors specifically designed for AI algorithm operations [ 21 , 22 , 23 , 24 , 25 ]. The accuracy of the vision-based monitoring system was analyzed to demonstrate the feasibility of the system.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Monitoring System with Privacy Preservation Based on Edge AI

Kim,

Park,

Jeong

et al. 2023

Micromachines

Self Cite

View full text Add to dashboard Cite

Currently, the trend of elderly people living alone is rising due to rapid aging and shifts in family structures. Accordingly, the efficient implementation and management of monitoring systems tailored for elderly people living alone have become paramount. Monitoring systems are generally implemented based on multiple sensors, and the collected data are processed on a server to provide monitoring services to users. Due to the use of multiple sensors and a reliance on servers, there are limitations to economical maintenance and a risk of highly personal information being leaked. In this paper, we propose an intelligent monitoring system with privacy preservation based on edge AI. The proposed system achieves cost competitiveness and ensures high security by blocking communication between the camera module and the server with an edge AI module. Additionally, applying edge computing technology allows for the efficient processing of data traffic. The edge AI module was designed with Verilog HDL and was implemented on a field-programmable gate array (FPGA). Through experiments conducted on 6144 frames, we achieved 95.34% accuracy. Synthesis results in a 180 nm CMOS technology indicated a gate count of 1516 K and a power consumption of 344.44 mW.

show abstract

“…Therefore, before designing a k-NN accelerator, it is necessary to review and optimize the structure of a k-NN accelerator suitable for a specific problem. It is similar to testing hardware logic in FPGA before application-specific integrated circuit (ASIC) [ 36 , 37 , 38 ]. Therefore, in order to overcome this problem, this study proposes an ASimOV framework that examines and optimizes various k-NN accelerators to generate HDL code that can be executed directly on the FPGA.…”

Section: Introductionmentioning

confidence: 99%

ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator

Hwang

Lee

2021

Micromachines

Self Cite

View full text Add to dashboard Cite

Artificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial intelligence accelerators. In this paper, we propose the ASimOV framework, which optimizes artificial intelligence algorithms and generates Verilog hardware description language (HDL) code for executing intelligence algorithms in field programmable gate array (FPGA). To verify ASimOV, we explore the performance space of k-NN algorithms and generate Verilog HDL code to demonstrate the k-NN accelerator in FPGA. Our contribution is to provide the artificial intelligence algorithm as an end-to-end pipeline and ensure that it is optimized to a specific dataset through simulation, and an artificial intelligence accelerator is generated in the end.

show abstract

Lossless Decompression Accelerator for Embedded Processor with GUI

Cited by 6 publications

References 31 publications

Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

Intelligent Monitoring System with Privacy Preservation Based on Edge AI

ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator

Contact Info

Product

Resources

About