Abstract—This paper describes the design of a high-performance unified SVM classifier circuit. The proposed circuit supports both linear and non-linear SVM classifications. In order to ensure efficient classification, a 48x96 or 64x64 sliding window with 20 window strides is used. We reduced the circuit size by sharing most of the resources required for both types of classification. We described the proposed unified SVM classifier circuit using the Verilog HDL and synthesized the gate-level circuit using 65nm standard cell library. The synthesized circuit consists of 661,261 gates, operates at the maximum operating frequency of 152 MHz and processes up to 33.8 640x480 image frames per second. Index Terms—Support vector machine, unified, hig...
Tongue body features such as colour is used in Traditional Chinese Medicine (TMC) practices to diagn...
This work presents an optimized architecture for cascaded SVM processing, along with a hardware redu...
International audienceUnderstanding the composition of the Internet traffic has many applications no...
A one-class support vector machine (OC-SVM) is implemented using an on-chip-trainable analog VLSI pr...
Simple hardware architecture for implementation of pairwise Support Vector Machine (SVM) classifiers...
The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to b...
Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the major...
Classifying Microarray data, which are of high dimensional nature, requires high computational power...
This masters thesis deals with algorithms for learning SVM classifiers on hardware systems and their...
Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the major...
Currently, high-level synthesis (HLS) methods and tools are a highly relevant area in the strategy o...
In recent years, Support Vector Machine (SVM) classifiers have played a crucial role in providing da...
Support Vector Machine (SVM) is a robust machine learning model used for efficient classification wi...
International audienceThe first aim of this work is to propose the design of a system-on-chip (SoC) ...
We propose a classification method based on a decision tree whose nodes consist of linear Support Ve...
Tongue body features such as colour is used in Traditional Chinese Medicine (TMC) practices to diagn...
This work presents an optimized architecture for cascaded SVM processing, along with a hardware redu...
International audienceUnderstanding the composition of the Internet traffic has many applications no...
A one-class support vector machine (OC-SVM) is implemented using an on-chip-trainable analog VLSI pr...
Simple hardware architecture for implementation of pairwise Support Vector Machine (SVM) classifiers...
The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to b...
Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the major...
Classifying Microarray data, which are of high dimensional nature, requires high computational power...
This masters thesis deals with algorithms for learning SVM classifiers on hardware systems and their...
Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the major...
Currently, high-level synthesis (HLS) methods and tools are a highly relevant area in the strategy o...
In recent years, Support Vector Machine (SVM) classifiers have played a crucial role in providing da...
Support Vector Machine (SVM) is a robust machine learning model used for efficient classification wi...
International audienceThe first aim of this work is to propose the design of a system-on-chip (SoC) ...
We propose a classification method based on a decision tree whose nodes consist of linear Support Ve...
Tongue body features such as colour is used in Traditional Chinese Medicine (TMC) practices to diagn...
This work presents an optimized architecture for cascaded SVM processing, along with a hardware redu...
International audienceUnderstanding the composition of the Internet traffic has many applications no...