基于FPGA的SVM分类器的黑色素瘤检测芯片系统
A system on chip for melanoma detection using FPGA-based SVM classifier
摘要：
支持向量机（Support Vector Machine，SVM）是一种鲁棒的机器学习模型，在不同的分类问题上表现出很高的准确性，被广泛应用于各种嵌入式应用中。然而，嵌入式SVM分类器的实现是具有挑战性的，由于固有的复杂计算所需的。这促使在硬件平台上实现SVM，以实现低成本和低功耗的高性能计算。黑色素瘤是皮肤癌中最具侵袭性的一种，会增加死亡率。我们的目标是开发一种优化的嵌入式SVM分类器，专用于低成本手持设备，用于初级医疗保健中黑色素瘤的早期检测。在本文中，我们提出了一种硬件/软件协同设计，用于将SVM分类器实现到FPGA上，从而在芯片上实现黑色素瘤检测。在最近的混合FPGA（Zynq）平台上实现的SVM利用现代UltraFast高级合成设计方法实现了芯片上的高效黑色素瘤分类。硬件实现结果表明，分类准确率为97.9%，和一个显着的硬件加速率为21，只有3%的资源利用率和1.69W的功耗。这些结果表明，实现的片上系统满足高性能和低资源利用率，功耗和成本的关键嵌入式系统的约束，同时实现高效的分类与高分类精度。
英文摘要：
Support Vector Machine (SVM) is a robust machine learning model that shows high accuracy with different classification problems, and is widely used for various embedded applications. However , implementation of embedded SVM classifiers is challenging, due to the inherent complicated computations required. This motivates implementing the SVM on hardware platforms for achieving high performance computing at low cost and power consumption. Melanoma is the most aggressive form of skin cancer that increases the mortality rate. We aim to develop an optimized embedded SVM classifier dedicated for a low-cost handheld device for early detection of melanoma at the primary healthcare. In this paper, we propose a hardware/software co-design for implementing the SVM classifier onto FPGA to realize melanoma detection on a chip. The implemented SVM on a recent hybrid FPGA (Zynq) platform utilizing the modern UltraFast High-Level Synthesis design methodology achieves efficient melanoma classification on chip. The hardware implementation results demonstrate classification accuracy of 97.9%, and a significant hardware acceleration rate of 21 with only 3% resources utilization and 1.69W for power consumption. These results show that the implemented system on chip meets crucial embedded system constraints of high performance and low resources utilization, power consumption, and cost, while achieving efficient classification with high classification accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
