Heuijee Yun (Integrated Ph.D. Candidate)
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Integrated Ph.D. Candidate. |
Repository Commit History
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Introduction
Full Bio Sketch
Ms. Yun received her B.S. Degree in Electronics Engineering at Kyungpook National University, Daegu, Korea in 2022. She is currently an Integrated Ph.D. student in School of Electronic and Electrical Engineering at Kyungpook National University, Daegu, Republic of Korea. Her research interests include image processing that can be implemented on a lightweight embedded board. Also, she has been conducting various simulations and light-weight image processing required for autonomous driving using deep learning and parallel processing of FPGA. She is currently researching about algorithm of low-power for image processing in an autonomous driving ADAS system. She received 2024 KNU-EE Funded Excellence Ph.D Award (10,000,000 Won Scholarship).
Research Topic
LiDAR Signal Processing
Among the functions of self-driving cars, avoidance after object recognition is important. Because camera data alone is insufficient to recognize and avoid people or obstacles, we train objects as deep neural networks with data combined with LiDAR data. Then we can apply the educated weight using Yolo, Tensorflow and Opencv for object detection. As a result, Obstacle avoidance algorithms can be executed more accurately and faster.
FPGA-based Oject Detection for Autonomous Driving
Parallelization of image processing for low-power implement - Currently, the board used for autonomous driving is equivalent to a single computer. While various studies are being conducted in the direction of using lightweight FPGAs for efficiency, we study data of image sensors, which are essential functions for ADAS systems in autonomous vehicles. When a huge amount of image data is input, a lightweight algorithm is studied so that the data can be used in a lightweight FPGA. The lane recognition algorithm is largely composed of two flows, canny edge detection and hough transform. At this time, canny edge detection passes through several filters, and at this time, many matrix operations must use several resources. We parallelized this task on a pixel basis and implemented it using a hardware language with low power, small runtime, and constant accuracy.
Currently, with the active development of autonomous driving, several technologies corresponding to it are developing. An essential technology to achieve a high level of autonomous driving is image processing technology. Since the camera input is an essential element, the key is how to implement it in a lightweight vehicle processor. Therefore, we study a lightweight image processing method using parallel processing so that it can be executed on a lightweight embedded board. There are two major algorithms required for lane recognition: Canny edge detection and Hough transformation. Hough transformation cannot be parallelized because all pixels must be read due to the nature of the algorithm, and Canny edge detection is parallelized. After completing the grayscale conversion, the gaussian smoothing, sobel operator, non-maximum suppression and hysteresis parts can be parallelized. Since this part requires filter operation, the corresponding pixel must be determined and parallelized. The pixels in each thread must be at least 5 wide because the Gaussian filter is 5x5. Through this parallelization, efficient results can be obtained in terms of memory and time, and accordingly, a lightweight lane recognition algorithm can be implemented
On-Chip Instruction Execution Acceleration for AI Processors
Recently, it has become possible to train neural networks on MPUs to achieve high performance and reduce power consumption. However, analyzing and processing the massive amounts of data used in deep learning is only being done on better performing multicore microprocessors. ARM-based cores have introduced the concept of single instruction, multiple data (SIMD), which plays an important role in optimizing the performance of deep learning algorithms. SIMD is a parallel processing technique classified according to Flynn's taxonomy. However, SIMD is only available on certain ARM cores and compilers, and it increases the size of the bus because it sends and receives 128-bit data. It also requires vectorization of the input data, which requires resources for preprocessing. Therefore, we introduce an implementation of micro-SIMD on the ARM Cortex M0 structure. Although the original ARM Cortex M0 does not have a SIMD, we generated and executed 16-bit instructions directly. Neural network training algorithms such as CNNs require a huge number of loops and MAC operations for each training layer. The parallelism of micro-SIMD can be very effective in computations like this, where the same operations are performed repeatedly.
Deep Learning based Human Detection using Thermal-RGB Data Fusion
As the number of drivers increases every year, so does the number of traffic fatalities. In Korea, pedestrian accidents accounted for 35.5% of all traffic accidents in the last two years, and the number of accidents involving children is increasing every year. Currently, self-driving cars rely on lidar, which can only recognize obstacles in the distance, making it inadequate for accident prevention. To reduce these accidents, we propose selective thermal data that can identify people beyond the limited field of view. We first utilize RGB camera image data for object recognition. In the presence of vehicles or obstacles, we selectively use thermal data. The thermal data can only identify people, which is used to prevent unexpected accidents. The RGB image is divided into thirds and each section is evaluated for obstacles, prioritizing the areas with the most obstacles for integration with thermal data. Using the algorithm described, the accuracy increased by a factor of 2.07, from 40.43% to 83.91%. In addition, experiments performed on a personal computer show that the algorithm can operate in real time at a rate of 2.7 frames per second, using 175.95 megabytes of memory for 0.36 seconds per image. When running the algorithm on a lightweight board such as the Jetson Nano, it runs at a rate of 0.75 frames per second, using 140.08 megabytes of memory for 1.33 seconds per image.
Spike Neural Network SoC Implementation
SNNs are a type of artificial neural network (ANN) that mimic the way brain neural networks process information. They use spikes as the unit of information, which propagate through a network of neurons and synapses. Spikes exchange only discrete information about whether a spike occurred in a specific neuron at a specific time, as opposed to tensors or floats in existing deep learning networks such as MLP, RNN, and CNN. The Convolutional Spiking Neural Network structure can operate with fewer electrical signals and is more energy efficient than deep neural networks (DNN) and convolutional neural networks (CNN) because it consumes less power. However, this comes at the cost of lower accuracy. To address this issue, we propose a structure that computes multiple Convolutional layers in parallel by classifying them according to the patterns in the input dataset. This structure creates parallel layers based on the input class and prunes the processing element (PE) units to fit each input. The resulting structure is more accurate and can be trained on lightweight hardware.
Publications
Journal Publications (SCI 4, KCI 2)
Heuijee Yun and Daejin Park. Virtualization of Self-Driving Algorithms by Interoperating Embedded Controllers on Game Engine for Digital Twining Autonomous Vehicle (SCI) Electronics, 2021.
Heuijee Yun and Daejin Park. Efficient Object Recognition by Masking Semantic Pixel Difference Region of Vision Snapshot for Lightweight Embedded Systems (KCI) Journal of the Korea Institute of Information and Communication Engineering, 26(6):813-826, 2022.
Heuijee Yun and Daejin Park. Efficient Object Detection based on Masking Semantic Segmentation Region for Lightweight Embedded Processors (SCI) Sensors, 22(22):8890-8911, 2022.
Heuijee Yun and Daejin Park. Low-Power Lane Detection Unit with Sliding-based Parallel Segment Detection Accelerator for Lightweighted Automotive Microcontrollers (SCI) IEEE Access, 12:4339-4353, 2024.
Heuijee Yun and Daejin Park. High-Speed Energy-Efficient Model based Dynamic Pruning using Pattern-based Alignment for Convolutional Spiking Neural Network Hardware Accelerators (KCI) IEMEK Journal of Embedded Systems and Applications, 2024.
Heuijee Youn and Daejin Park. S3A-NPU: A High-Performance Hardware Accelerator for Spiking Self-Supervised Learning with Dynamic Adaptive Memory Optimization (SCI) (Under Review) IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2024.
Conference Publications (Intl. 8)
Heuijee Yun and Daejin Park. Simulation of Self-driving System by implementing Digital Twin with GTA5 In IEEE ICEIC 2021, 2021.
Heuijee Yun and Daejin Park. Yolo-based Realtime Object Detection using Interleaved Redirection of Time-Multiplxed Streamline of Vision Snapshot for Lightweighted Embedded Processors In 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2021.
Heuijee Yun and Daejin Park. Mitigating Overflow of Object Detection Tasks Based on Masking Semantic Difference Region of Vision Snapshot for High Efficiency In 2022 IEEE International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2022.
Heuijee Yun and Daejin Park. FPGA Realization of Lane Detection Unit using Sliding-based Parallel Segment Detection for Buffer Memory Reduction In IEEE ICCE 2023, 2023.
Heuijee Yun and Daejin Park. Low-Power Parallel Lane Detection Unit for Lightweight Automotive Processors In IEEE COOLChips 2023, 2023.
Jisu Kwon, Heuijee Yun, and Daejin Park. Dynamic MAC Unit Pruning Techniques in Runtime RTL Simulation for Area-Accuracy Efficient Implementation of Neural Network Accelerator In IEEE MWSCAS 2023, 2023.
Heuijee Yun and Daejin Park. Parallel Processing of 3D Object Recognition by Fusion of 2D Images and LiDAR for Autonomous Driving In International Conference on Electronics, Information, and Communication (ICEIC 2024), 2024.
Heuijee Yun and Daejin Park. Deep Learning based Human Detection using Thermal-RGB Data Fusion for Safe Automotive Guided-Driving In IEEE International Conference on Pervasive Computing and Communication (Percom 2024 - Pervehicle), 2024.
Heuijee Yun and Daejin Park. Preparing IEEE COOLChips, 2025.
Heuijee Yun and Daejin Park. Preparing IEEE DAC, 2025.
Heuijee Yun and Daejin Park. Preparing IEEE HPDC, 2025.
Heuijee Yun and Daejin Park. Preparing IEEE ESWEEEKS's CASES, 2025.
Patents (Total 7 Patents)
Autonomous Driving Algorithm Simulation Method and System based on Game Engine In Korea Patent and Tradmark Office, Korea Patent 10-260183, 2024
Real-Time Object Detection System and Method using Interleaving of Snapshots In Korea Patent and Tradmark Office, June 2022. Korea Patent Pending
System and Method for Recognizing Object Included in Image In Korea Patent and Tradmark Office, June 2022. Korea Patent Pending
Compiling Device and Method for Spiking Neural Network Model In Korea Patent and Tradmark Office, Dec 2024. Korea Patent Pending
Accelerator for Spiking Self-Supervised Learning Model and Computational Method of the Accelerator In Korea Patent and Tradmark Office, Dec 2024. Korea Patent Pending
FPGA-based Lane Detection Accelerator and Lane Detection Method In Korea Patent and Tradmark Office, Dec 2024. Korea Patent Pending
Pedestrian Detection Method and Pedestrian Detection Device Performing the Same In Korea Patent and Tradmark Office, Dec 2024. Korea Patent Pending
Participation in International Conference
IEEE ICEIC 2021, Jeju, Korea
IEEE ISPACS 2021, Jeju, Korea
IEEE ICAIIC 2022, Jeju, Korea
ACM Micro 2022, Chicago, USA
ACM Multimedia ASIA 2022, Tokyo, Japan
IEEE ICCE 2023, Las Vegas, USA
IEEE COOLChips 2023, Tokyo, Japan
IEEE MWSCAS 2023, Arizona, USA
IEEE ICEIC 2024, Taipei, Taiwan
IEEE Percom 2024, Biarritz, France
IEEE ISLPED 2024, California, USA
IEEE COOLChips 2025, Tokyo, Japan
IEEE DAC 2025, San Francisco, USA
Last Updated, 2024.12.13
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