  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.
