Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Pulikala, A.Subject: search.filters.subject.Machine Learning

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    Hardware-Optimized Deep Learning Model for FPGA-Based Character Recognition
    (Institute of Electrical and Electronics Engineers Inc., 2023) Rao, P.S.; Pulikala, A.
    Deep neural networks (DNNs) are widely used algorithms in machine learning. Even though most of the deep learning applications are driven by software solutions, there has been significant research and development aimed at optimizing these algorithms over the years. However, when considering hardware implementation applications, it becomes essential to optimize the design not only in software but also in hardware. In this paper, we present a straightforward yet effective Convolutional Neural Network architecture that is meticulously optimized both in hardware and software for char-acter recognition applications. The implemented accelerator was realized on a Xilinx Zynq XC7Z020CLG484 FPGA using a high-level synthesis tool. To enhance performance, the accelerator employs an optimized fixed-point data type and applies loop parallelization techniques combining 2D convolution and 2D max pooling operations. The hardware efficiency of the proposed DNN is compared with some of the existing architectures in terms of hardware utilization. © 2023 IEEE.
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    Robustness Analysis of EV Charging System using Random Forest Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2023) Barre, U.P.V.; Satyanarayan, S.; Reddy, H.; Pulikala, A.; Bajaj, A.
    Electric cars offer numerous benefits and are considered the future of the automobile industry. However, their worldwide adoption still needs to grow. One of the primary reasons for this delay in electrification is charge anxiety, which refers to the uncertainty customers feel when connecting the charging cable to the car. To address this issue, this study analyses the performance of the charging system using a machine learning model to identify sensitive signals that influence the charging process and can cause successful charging or charge termination. The analysis will also help to define robust operating regions where the charging component can reliably function, regardless of external conditions. This study's findings will provide insights into electric vehicle charging behavior with the supply station. © 2023 IEEE.