scholarly journals High-Level Synthesis of Multiclass SVM Using Code Refactoring to Classify Brain Cancer from Hyperspectral Images

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1494 ◽  
Author(s):  
Abelardo Baez ◽  
Himar Fabelo ◽  
Samuel Ortega ◽  
Giordana Florimbi ◽  
Emanuele Torti ◽  
...  
Keyword(s):  
Brain Cancer ◽  
Research Field ◽  
Hyperspectral Images ◽  
High Level Synthesis ◽  
Support Vector ◽  
Svm Classifier ◽  
Code Refactoring ◽  
Field Programmable ◽  
And Performance ◽  
High Level

Currently, high-level synthesis (HLS) methods and tools are a highly relevant area in the strategy of several leading companies in the field of system-on-chips (SoCs) and field programmable gate arrays (FPGAs). HLS facilitates the work of system developers, who benefit from integrated and automated design workflows, considerably reducing the design time. Although many advances have been made in this research field, there are still some uncertainties about the quality and performance of the designs generated with the use of HLS methodologies. In this paper, we propose an optimization of the HLS methodology by code refactoring using Xilinx SDSoCTM (Software-Defined System-On-Chip). Several options were analyzed for each alternative through code refactoring of a multiclass support vector machine (SVM) classifier written in C, using two different Zynq®-7000 SoC devices from Xilinx, the ZC7020 (ZedBoard) and the ZC7045 (ZC706). The classifier was evaluated using a brain cancer database of hyperspectral images. The proposed methodology not only reduces the required resources using less than 20% of the FPGA, but also reduces the power consumption −23% compared to the full implementation. The speedup obtained of 2.86× (ZC7045) is the highest found in the literature for SVM hardware implementations.

scholarly journals Algorithmic-Level Approximate Tensorial SVM Using High-Level Synthesis on FPGA

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 205
Author(s):  
Hamoud Younes ◽  
Ali Ibrahim ◽  
Mostafa Rizk ◽  
Maurizio Valle
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
High Level Synthesis ◽  
Support Vector ◽  
Svm Classifier ◽  
Approximate Computing ◽  
Classification Framework ◽  
Time Latency ◽  
High Level

Approximate Computing Techniques (ACT) are promising solutions towards the achievement of reduced energy, time latency and hardware size for embedded implementations of machine learning algorithms. In this paper, we present the first FPGA implementation of an approximate tensorial Support Vector Machine (SVM) classifier with algorithmic level ACTs using High-Level Synthesis (HLS). A touch modality classification framework was adopted to validate the effectiveness of the proposed implementation. When compared to exact implementation presented in the state-of-the-art, the proposed implementation achieves a reduction in power consumption by up to 49% with a speedup of 3.2×. Moreover, the hardware resources are reduced by 40% while consuming 82% less energy in classifying an input touch with an accuracy loss less than 5%.

scholarly journals Development of Multiobjective High-Level Synthesis for FPGAs

2020 ◽  
Vol 2020 ◽  
pp. 1-25
Author(s):  
Darian Reyes Fernandez de Bulnes ◽  
Yazmin Maldonado ◽  
Leonardo Trujillo
Keyword(s):  
Design Space Exploration ◽  
Optimization Methods ◽  
Digital Circuit ◽  
High Level Synthesis ◽  
Objective Functions ◽  
Trade Offs ◽  
Field Programmable ◽  
Digital Circuit Designs ◽  
And Performance ◽  
High Level

Traditionally, the High-Level Synthesis (HLS) for Field Programmable Gate Array (FPGA) devices is a methodology that transforms a behavioral description, as the timing-independent specification, to an abstraction level that is synthesizable, like the Register Transfer Level. This process can be performed under a framework that is known as Design Space Exploration (DSE), which helps to determine the best design by addressing scheduling, allocation, and binding problems, all three of which are NP-hard problems. In this manner, and due to the increased complexity of modern digital circuit designs and concerns regarding the capacity of the FPGAs, designers are proposing novel HLS techniques capable of performing automatic optimization. HLS has several conflicting metrics or objective functions, such as delay, area, power, wire length, digital noise, reliability, and security. For this reason, it is suitable to apply Multiobjective Optimization Algorithms (MOAs), which can handle the different trade-offs among the objective functions. During the last two decades, several MOAs have been applied to solve this problem. This paper introduces a comprehensive analysis of different MOAs that are suitable to perform HLS for FPGA devices. We highlight significant aspects of MOAs, namely, optimization methods, intermediate structures where the optimizations are performed, HLS techniques that are addressed, and benchmarks and performance assessments employed for experimentation. In addition, we show the analysis of how multiple objectives are optimized currently in the algorithms and which are the objective functions that are optimized. Finally, we provide insights and suggestions to contribute to the solution of major research challenges in this area.

A High-Throughput Architecture for the SHA-256/224 Compliant With the DSRC Standard

2019 ◽  
Vol 10 (1) ◽  
pp. 98-118
Author(s):  
Imed Saad Ben Dhaou ◽  
Hannu Tenhunen
Keyword(s):  
High Throughput ◽  
Short Range ◽  
State Of The Art ◽  
High Level Synthesis ◽  
Data Flow Graph ◽  
Transformation Techniques ◽  
Safety Message ◽  
Dedicated Short Range Communication ◽  
Field Programmable ◽  
High Level

This article presents a word serial retimed architecture for the SHA-256/224 algorithm. The architecture is compliant with the dedicated-short range communication for safety message authentications. We elaborate three-operand adder architectures suitable for field programmable gate array implementation. Several transformation techniques at the data-flow-graph level have been used to derive the architecture. Synthesis results show that the architecture has high throughput/ slice value compared with state-of-the-art SHA-256 implementations. The article also promulgates a comparison between high-level synthesis and RTL design.

scholarly journals Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1443
Author(s):  
Mai Ramadan Ibraheem ◽  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Mohammed Elmogy
Keyword(s):  
Area Under The Curve ◽  
Malignant Neoplasm ◽  
Texture Features ◽  
Feature Space ◽  
Skin Neoplasms ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormal Growth ◽  
Malignant Skin ◽  
High Level

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

scholarly journals SVM-based switching filter hardware design for mixed noise reduction in digital images using high-level synthesis tools

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Image Quality ◽  
Support Vector Machine Classifier ◽  
Digital Images ◽  
High Level Synthesis ◽  
Support Vector ◽  
Hardware Accelerator ◽  
Mixed Noise ◽  
High Level ◽  
Switching Filter ◽  
Better Than

Impulse and Gaussian are the two most common types of noise that affect digital images due to imperfections in the imaging process, compression, storage and communication. The conventional filtering approaches, however, reduce the image quality in terms of sharpness and resolution while suppressing the effects of noise. In this work, a machine learning-based filtering structure has been proposed preserves the image quality while effectively removing the noise. Specifically, a support vector machine classifier is employed to detect the type of noise affecting each pixel to select an appropriate filter. The choice of filters includes Median and Bilateral filters of different kernel sizes. The classifier is trained using example images with known noise parameters. The proposed filtering structure has been shown to perform better than the conventional approaches in terms of image quality metrics. Moreover, the design has been implemented as a hardware accelerator on an FPGA device using high-level synthesis tools.

AN EVOLUTIONARY ALGORITHM FOR THE ALLOCATION PROBLEM IN HIGH-LEVEL SYNTHESIS

2005 ◽  
Vol 14 (02) ◽  
pp. 347-366 ◽  
Author(s):  
HAIDAR M. HARMANANI ◽  
RONY SALIBA
Keyword(s):  
Evolutionary Algorithm ◽  
Allocation Problem ◽  
High Level Synthesis ◽  
Gate Arrays ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Functional Units ◽  
High Level ◽  
The Cost ◽  
Short Time

This paper presents an evolutionary algorithm to solve the datapath allocation problem in high-level synthesis. The method performs allocation of functional units, registers, and multiplexers in addition to controller synthesis with the objective of minimizing the cost of hardware resources. The system handles multicycle functional units as well as structural pipelining. The proposed method was implemented using C++ on a Linux workstation. We tested our method on a set of high-level synthesis benchmarks, all yielding good solutions in a short time. An integration path to Field Programmable Gate Arrays (FPGAs) is provided through VHDL.

Improving Human Activity Recognition in Smart Homes

2015 ◽  
Vol 6 (3) ◽  
pp. 19-37 ◽  
Author(s):  
M'Hamed Bilal Abidine ◽  
Lamya Fergani ◽  
Belkacem Fergani ◽  
Anthony Fleury
Keyword(s):  
Human Activity ◽  
Conditional Random Fields ◽  
Imbalanced Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Discriminant ◽  
Previous State ◽  
High Level ◽  
The Cost ◽  
New Criterion

Even if it is now simple and cheap to collect sensors information in a smart home environment, the main issue remains to infer high-level activities from these simple readings. The main contribution of this work is twofold. Firstly, the authors demonstrate the efficiency of a new procedure for learning Optimized Cost-Sensitive Support Vector Machines (OCS-SVM) classifier based on the user inputs to appropriately tackle the problem of class imbalanced data. It uses a new criterion for the selection of the cost parameter attached to the training errors. Secondly, this method is assessed and compared with the Conditional Random Fields (CRF), Linear Discriminant Analysis (LDA), k-Nearest Neighbours (k-NN) and the traditional SVM. Several and various experimental results obtained on multiple real world human activity datasets using binary and ubiquitous sensors show that OCS-SVM outperforms the previous state-of-the-art classification approach.

scholarly journals Identification of Leaf-Scale Wheat Powdery Mildew (Blumeria graminis f. sp. Tritici) Combining Hyperspectral Imaging and an SVM Classifier

Plants ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 936 ◽  
Author(s):  
Jinling Zhao ◽  
Yan Fang ◽  
Guomin Chu ◽  
Hao Yan ◽  
Lei Hu ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Hyperspectral Imaging ◽  
Blumeria Graminis ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Identification Accuracy ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Measurement Technology

Powdery mildew (PM, Blumeria graminis f. sp. tritici) is a devastating disease for wheat growth and production. It is highly meaningful that the disease severities can be objectively and accurately identified by image visualization technology. In this study, an integral method was proposed based on a hyperspectral imaging dataset and machine learning algorithms. The disease severities of wheat leaves infected with PM were quantitatively identified based on hyperspectral images and image segmentation techniques. A technical procedure was proposed to perform the identification and evaluation of leaf-scale wheat PM, specifically including three primary steps of the acquisition and preprocessing of hyperspectral images, the selection of characteristic bands, and model construction. Firstly, three-dimensional reduction algorithms, namely principal component analysis (PCA), random forest (RF), and the successive projections algorithm (SPA), were comparatively used to select the bands that were most sensitive to PM. Then, three diagnosis models were constructed by a support vector machine (SVM), RF, and a probabilistic neural network (PNN). Finally, the best model was selected by comparing the overall accuracies. The results show that the SVM model constructed by PCA dimensionality reduction had the best result, and the classification accuracy reached 93.33% by a cross-validation method. There was an obvious improvement of the identification accuracy with the model, which achieved an 88.00% accuracy derived from the original hyperspectral images. This study can provide a reference for accurately estimating the disease severity of leaf-scale wheat PM and other plant diseases by non-contact measurement technology.

scholarly journals Extraction of tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu via spectral-spatial classification using UAV-based hyperspectral images

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Ning Zhang ◽  
Yueting Wang ◽  
Xiaoli Zhang
Keyword(s):  
Forest Health ◽  
Hyperspectral Images ◽  
Classification Method ◽  
Pinus Tabulaeformis ◽  
Disease Assessment ◽  
Support Vector ◽  
Svm Classifier ◽  
Edge Preserving ◽  
Spatial Classification ◽  
Tree Crowns

Abstract Background Tree crown extraction is an important research topic in forest resource monitoring. In particular, it is a prerequisite for disease detection and mapping the degree of damage caused by forest pests. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is effective for surveying and monitoring forest health. This article proposes a spectral-spatial classification framework that uses UAV-based hyperspectral images and combines a support vector machine (SVM) with an edge-preserving filter (EPF) for completing classification more finely to automatically extract tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu (D. tabulaeformis) in Jianping county of Liaoning province, China. Results Experiments were conducted using UAV-based hyperspectral images, and the accuracy of the results was assessed using the mean structure similarity index (MSSIM), the overall accuracy (OA), kappa coefficient, and classification accuracy of damaged Pinus tabulaeformis. Optimized results showed that the OA of the spectral-spatial classification method can reach 93.17%, and the extraction accuracy of damaged tree crowns is 7.50–9.74% higher than that achieved using the traditional SVM classifier. Conclusion This study is one of only a few in which a UAV-based hyperspectral image has been used to extract tree crowns damaged by D. tabulaeformis. Moreover, the proposed classification method can effectively extract damaged tree crowns; hence, it can serve as a reference for future studies on both forest health monitoring and larger-scale forest pest and disease assessment.

SUPPORT VECTOR CLASSIFICATION OF LAND COVER AND BENTHIC HABITAT FROM HYPERSPECTRAL IMAGES

2008 ◽  
Vol 18 (02) ◽  
pp. 337-348 ◽  
Author(s):  
VIDYA MANIAN ◽  
MIGUEL VELEZ-REYES
Keyword(s):  
Land Cover ◽  
Hyperspectral Image ◽  
Feature Vector ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Benthic Habitat ◽  
Hyperspectral Image Classification ◽  
Habitat Classification

This paper presents a novel wavelet and support vector machine (SVM) based method for hyperspectral image classification. A 1-D wavelet transform is applied to the pixel spectra, followed by feature extraction and SVM classification. Contrary to the traditional method of using pixel spectra with SVM classifier, our approach not only reduces the dimension of the input pixel feature vector but also improves the classification accuracy. Texture energy features computed in the spectral dimension are mapped using polynomial kernels and used for training the SVM classifier. Results with AVIRIS and other hyperspectral images for land cover and benthic habitat classification are presented. The accuracy of the method with limited training sets and computational burden is assessed.

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