Category: neural network

Categories
kecerdasan artifisial neural network

Mobile-based Primate Image Recognition using CNN

Abstract

Six out of 25 species of primates most endangered are in Indonesia. Six of these primates are namely Orangutan, Lutung, Bekantan, Tarsius tumpara, Kukang, and Simakobu. Three of the six primates live mostly on the island of Borneo. One form of preservation of primate treasures found in Kalimantan is by conducting studies on primate identification. In this study, an android app was developed using the CNN method to identify primate species in Kalimantan wetlands. CNN is used to extract spatial features from primate images to be very efficient for image identification problems. The data set used in this study is ImageNets, while the model used is MobileNets. The application was tested using two scenarios, namely using photos and video recordings. Photos were taken directly, then reduced to a resolution of 256 x 256. Then, videos were taken in approximately 10 to 30 seconds with two megapixel camera resolution. The results obtained was an average accuracy of 93.6% when using photos and 79% when using video recordings. After calculating the accuracy, the usability test using SUS was performed. Based on the SUS results, it is known that the application developed is feasible to use.

[https://jurnal.ugm.ac.id/ijccs/article/view/65640]

Categories
FPGA neural network

High-Level Synthesize of Backpropagation Artificial Neural Network Algorithm on the FPGA

by Afianah, N., Putra, A.E., and Dharmawan, A.

The studies related to the synthesis of backpropagation artificial neural network algorithms are still based on the direct synthesis, so it requires an effort to modify the algorithm into hardware language so it can be optimized, synthesized and implemented into the FPGA. The High-Level Synthesis (HLS) is a software compiler which able to convert C specifications into Register Transfer Level (RTL) form, which can be synthesized into FPGAs. So the designer can focus on the optimization of the algorithm itself, including speed and resource optimization. This paper discus the results of the synthesis of backpropagation artificial neural network algorithms using HLS (High-Level Synthesis) software. The C-synthesis results based on the Zynq7000 FPGA showed an accuracy of 96.56%, were able to be clocked with a period of around 9.37 ns, with resource usage of 18% for BRAM_18K, 67% for DSP48E, 25% for FF and 71% for LUT. While the utilization difference is not significant compare to the previous studies, the optimization effort using an HLS tools need to be taken into account.

(DOI: 10.1109/ICST47872.2019.9166209)