Comparison of Performance of Optimized HSI CNN models on Desktop and Embedded Platforms

Abstract

We compare different platforms for inference of convolutional neural networks in this paper. We trained various neural networks to determine the material in the source hyperspectral cube. Then we convert them to inference format and compare the inference results. We used tools under Xilinx Vitis AI for FPGA implementation. We try to minimize the size of the proposed networks by pruning them and provide further comparisons. FPGA platforms show to be energy efficient but still slower than a graphics card in terms of performance.

FPGA Acceleration of a Composite Kernel SVM for Hyperspectral Image Classification

“…However, the accuracy of the algorithm was not verified although modified for hardware acceleration. In [14], a simple CNN for HSI classification was implemented and the performances on three different platforms, CPU, GPU, and FPGA, were compared. For the FPGA implementation, Xilinx Vitis AI tools were used.…”