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arXiv 2023年

作者： Baczmanski, MacIej Wasala, Mateusz Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics Agh University of Krakow Al. Mickiewicza 30 Krakow30-059 Poland

Perception and control systems for autonomous vehicles are an active area of scientific and industrial research. These solutions should be characterised by both high efficiency in recognising obstacles and other environmental elements in different road conditions, real-time capability, and energy efficiency. Achieving such functionality requires an appropriate algorithm and a suitable computing platform. In this paper, we have used the MultiTaskV3 detection-segmentation network as the basis for a perception system that can perform both functionalities within a single architecture. It was appropriately trained, quantised, and implemented on the AMD Xilinx Kria KV260 Vision AI embedded platform. By using this device, it was possible to parallelise and accelerate the computations. Furthermore, the whole system consumes relatively little power compared to a CPU-based implementation (an average of 5 watts, compared to the minimum of 55 watts for weaker CPUs, and the small size (119mm x 140mm x 36mm) of the platform allows it to be used in devices where the amount of space available is limited. It also achieves an accuracy higher than 97% of mAP (mean average precision) for object detection and above 90% of mIoU (mean intersection over union) score for image segmentation. The article also details the design of the Mecanum wheel vehicle, which was used to test the proposed solution in a mock-up city. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

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LiDAR-based drone navigation with reinforcement learning

arXiv

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arXiv 2023年

作者： Miera, Pawel Szolc, Hubert Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatably avoid trees and fly through the forest. © 2023, CC BY.

关键词： Robot Operating System

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Energy-Efficient Functional Safety Design Methodology Using ASIL Decomposition for Automotive Cyber-Physical systems

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IEEE Transactions on Reliability 2024年 1-23页

作者： Xie, Guoqi Peng, Hao Huang, Jing Li, Renfa Li, Keqin Department of Computer Engineering College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China and also with the Key Laboratory for Embedded and Cyber-Physical Systems of Hunan Province Changsha 410082 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China and also with the Department of Computer Science State University of New York New Paltz NY 12561 USA

Automotive cyber-physical systems (ACPS) are typical cyber-physical systems because of the joint interaction between the cyber part and physical part. Functional safety requirement (including response time and reliability requirements) for an ACPS function must be assured for safe driving. Auto industry is cost-sensitive, power-sensitive, and environment-friendly. Energy consumption affects the development efficiency of the ACPS and the living environment of people. This paper solves the problem of optimizing the energy consumption for an ACPS function while assuring its functional safety requirement (i.e., energy-efficient functional safety for ACPS). However, implementing minimum response time, maximum reliability, and minimum energy consumption is a conflicting problem. Consequently, solving the problem is a challenge. In this paper, we propose a three-stage design process toward energy-efficient functional safety for ACPS. The topic problem is divided into three sub-problems, namely, response time requirement verification (first stage), functional safety requirement verification (second stage), and functional safety-critical energy consumption optimization (third stage). The proposed energy-efficient functional safety design methodology is implemented by using automotive safety integrity level decomposition, which is defined in the ACPS functional safety standard ISO 26262. Experiments with real-life and synthetic ACPS functions reveal the advantages of the proposed design methodology toward energy-efficient functional safety for ACPS compared with state-of-the-art algorithms. IEEE

关键词： Energy utilization

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Fed-Haul: A Federated Learning Dual Band Point Multi-Point Backhaul Requirements in 5G Evolution and Beyond 23

Fed-Haul: A Federated Learning Dual Band Point Multi-Point B...

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6th International Conference on Networking, Intelligent systems and Security, NISS 2023

作者： Chehri, Abdellah Chaibi, Hasna Mettiti, Abderrahmane Saadane, Rachid Jeon, Gwanggil Dep. of Math. and Computer Science Royal Military College of Canada Kingston Canada Supmti Genius Laboratory Hassan Rabat Morocco It Rabat Center Faculty of Sciences Mohammed v University Rabat Morocco SIRC-LAGES-EHTP Hassania School of Public Works Casalanca Morocco Department of Embedded Systems Engineering Incheon National University Korea Republic of

ISBN: (纸本)9798400700194

The rapid adoption of smartphones and the explosive growth of data traffic due to these devices have been phenomenal. As the world anticipates more connected devices - the Internet of Things (IoT), vehicle-to-vehicle (V2V) communications, and wearable devices - and more value-added applications and services (ultra-high-definition video, 360° video, virtual reality, smart cars, etc.), leading industry experts are calling for the sixth generation (6G) networks. Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. Federated learning (FL) is essential in optimizing wireless communication networks' resources. On the other hand, wireless communications are crucial for FL. Therefore, the purpose of this survey paper is to bridge this gap in the literature by discussing the interdependency between FL and backhaul wireless communications. © 2023 ACM.

关键词： 5G mobile communication systems

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LiDAR-based drone navigation with reinforcement learning

TechRxiv

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TechRxiv 2023年

关键词： Drones

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Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

arXiv

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arXiv 2022年

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC – performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2022, CC BY.

关键词： System-on-chip

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Optical Character Recognition Using Optimized Convolutional Networks*

Optical Character Recognition Using Optimized Convolutional ...

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International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Anum Nawaz Muhammad Irfan Tomi Westerlund Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Turku Finland

Optical Character Recognition (OCR) has been a prominent area of research in pattern recognition for several decades, owing to its broad application potential in smart living. To improve offline OCR on mobile devices with limited computing resource, we have optimized Convolutional Neural Networks (CNNs) to efficiently detect text using minimal resources. To achieve this, we employed two distinct pretrained CNN models, namely AlexNet and Inception-V3, for feature extraction. Leveraging these models' unique characteristics and capabilities to extract diverse features, we aimed to enhance the classifier's accuracy. This, in turn, facilitates the development of an efficient edge-device application for faster and higher-quality OCR. Experimental results demonstrate that our proposed optimized algorithm outperforms existing CNN-based methods in the field of OCR, particularly in the categorization and detection of handwritten digits and character recognition. The conducted research yielded impressive accuracy results, with up to 97% accuracy on the MNIST dataset and 95.5% accuracy on the NIST dataset.

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Playing cards and bidding calls detection for automatic registration of a duplicate bridge game

TechRxiv

引用

TechRxiv 2022年

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

In this work, the implementation of a playing cards and bidding calls detection system for the automatic registration of a duplicate bridge game is presented. For this purpose, two YOLOv4 deep convolutional neural networks were used. During the training process, which was carried out using an automatically generated dataset, we obtained a detector characterised by more than 99.9% accuracy on the test set. The prepared system has been evaluated on videos collected during bridge competitions and is characterised by high accuracy. The application has been implemented and evaluated on a CPU and an embedded GPU. © 2022, CC BY.

关键词： Deep neural networks

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Towards real-time and energy efficient Siamese tracking - a hardware-software approach

TechRxiv

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TechRxiv 2022年

Siamese trackers have been among the state-of-the-art solutions in each Visual Object Tracking (VOT) challenge over the past few years. However, with great accuracy comes great computational complexity: to achieve real-time processing, these trackers have to be massively parallelised and are usually run on high-end GPUs. Easy to implement, this approach is energy consuming, and thus cannot be used in many low-power applications. To overcome this, one can use energy-efficient embedded devices, such as heterogeneous platforms joining the ARM processor system with programmable logic (FPGA). In this work, we propose a hardware-software implementation of the well-known fully connected Siamese tracker (SiamFC). We have developed a quantised Siamese network for the FINN accelerator, using algorithm-accelerator co-design, and performed design space exploration to achieve the best efficiency-to-energy ratio (determined by FPS and used resources). For our network, running in the programmable logic part of the Zynq UltraScale+ MPSoC ZCU104, we achieved the processing of almost 50 frames-per-second with tracker accuracy on par with its floating point counterpart, as well as the original SiamFC network. The complete tracking system, implemented in ARM with the network accelerated on FPGA, achieves up to 17 fps. These results bring us towards bridging the gap between the highly accurate but energy-demanding algorithms and energy-efficient solutions ready to be used in low-power, edge systems. © 2022, CC BY.

关键词： Field programmable gate arrays (FPGA)

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Towards real-time and energy efficient Siamese tracking - a hardware-software approach

arXiv

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arXiv 2022年

关键词： Energy efficiency

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