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Open Source SoC Design for Low-Cost Micro Weather Station 10

Open Source SoC Design for Low-Cost Micro Weather Station

引用

10th ieee International symposium on Smart Electronic Systems, iSES 2024

作者： Gupta, Namit Pathania, Pravar Goel, Keshav Sharma, Tarun Deb, Sujay Indraprastha Institute of Information Technology Delhi Dept. Of Electronics and Communication New Delhi India

ISBN: (纸本)9798331533229

This paper proposes a modular SoC, allowing for the dynamic addition or removal of peripherals such as I2C, UART, and other interfaces. Our current implementation targets a remote weather station as a case study, utilizing an Ibex core to interface with a BMP280 pressure sensor, an AHT10 temperature and humidity sensor, and an ESP8266 module for Wi-Fi connectivity to upload data to ThingSpeak for real-time monitoring. It also provides a contrast between collected data and regional data. The proposed SoC design enhances flexibility and cost-effectiveness by leveraging the open-source Ibex core, eliminating the need for paid licenses and toolchains. FPGA-based prototyping ensures robust verification and functional correctness of the designed SoC. The results demonstrate the potential of the proposed SoC platform for a wide range of embedded applications beyond environmental monitoring. © 2024 ieee.

关键词： Humidity sensors

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ESNet: An Efficient real-time Semantic Segmentation Network 22

ESNet: An Efficient Real-time Semantic Segmentation Network

引用

22nd ieee International symposium on Parallel and Distributed Processing with applications, ISPA 2024

作者： Xie, Renping He, Cong Tao, Ming Ding, Kai Dongguan University of Technology School of Computer Science and Technology Dongguan China

ISBN: (纸本)9798331509712

Efficient image segmentation algorithms are critical in computer vision, as they maintain high processing speeds while handling large amounts of data and providing practical solutions in resource-limited environments. While existing classical segmentation methods achieve good results, their real-time performance can be further improved. To address this issue, we propose an efficient segmentation network (ESNet) to capture extensive contextual information and improve segmentation fineness, which are two main issues in semantic segmentation, while ensuring real-time capability. Then, we first introduce an efficient context module (ECM) that effectively enlarges the effective receptive field (ERF) of the model and improves its performance in integrating contextual information. Subsequently, a skip connection with a simple feature fusion module (SFFM) is designed to provide rich detail information, thereby improving segmentation fineness. The efficacy of ESNet was evaluated on the PASCAL VOC2012 semantic segmentation dataset against several state-of-the-art methods. ESNet achieves 71.4 FPS at a resolution of 1024×2048 on a single NVIDIA GeForce RTX 3090 GPU and achieves 62.42% mIoU at a resolution of 320×480 with an extremely simple training recipe, striking a fine balance between segmentation accuracy and inference speed. © 2024 ieee.

关键词： Semantic Segmentation

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20.3 A 23.9TOPS/W @ 0.8V, 130TOPS AI Accelerator with 16× Performance-Accelerable Pruning in 14nm Heterogeneous embedded MPU for real-time Robot applications

20.3 A 23.9TOPS/W @ 0.8V, 130TOPS AI Accelerator with 16× P...

引用

2024 ieee International Solid-State Circuits Conference, ISSCC 2024

作者： Nose, Koichi Fujii, Taro Togawa, Katsumi Okumura, Shunsuke Mikami, Kentaro Hayashi, Daichi Tanaka, Teruhito Toi, Takao Renesas Electronics Tokyo Japan

ISBN: (纸本)9798350306200

To solve social problems such as labor shortages, there are growing expectations for the advancement of human-cooperative robots. In such robots, advanced environmental recognition (mainly AI based), planning and control (normally non-AI algorithms) have to be processed simultaneously in real time. However, with current AI chips, meeting these objectives is challenging for several reasons: 1) A high-performance AI accelerator with 100TOPS peak class is required, in particular when a multi-camera system is employed for wide-range environment recognition. However, such an accelerator cannot be incorporated in a robot device because its power exceeds 10W [1] and a huge fan is required. 2) embedded CPUs do not have sufficient performance for processing multiple robot tasks that mix AI and non-AI tasks in real-time. To solve these issues, we propose a power-efficient AI-MPU (microprocessor unit) including: 1) a flexible pruning rate control (named flexible N:M pruning) technology capable of up to 16× AI performance acceleration, and 2) a heterogenous architecture for multi-task & real-time robot operation based on the co-operation among a dynamically reconfigurable processor (DRP), AI accelerator (DRP-AI) and embedded CPU. © 2024 ieee.

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Monitoring Workers Safety in Restricted Working Areas: a Case-Study with LoRaWAN technology 19

Monitoring Workers Safety in Restricted Working Areas: a Cas...

引用

19th ieee Sensors applications symposium (SAS)

作者： Bertocco, Matteo Peruzzi, Giacomo Pozzebon, Alessandro Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9798350369267;9798350369250

This paper addresses the usability of wireless transmission technologies, for giving connectivity to wireless monitoring systems aimed at safeguarding workers operating within areas restricted for safety reasons, like human-dangerous spaces, or underground tanks. In such areas, critical conditions may arise, so that sensing is required in order to prevent incidents. For instance, possible emission of poisonous gases could be detected to ensure healthy conditions. To this aim, workers can be provided with portable Internet of Things (IoT) sensing devices in charge of measuring a set of physical and environmental parameters. The corresponding collected data is transmitted in real-time to a centre responsible for logging the collected information and engaging proper emergency procedures. Since, restricted areas may be built with materials intrinsically able to shield the electromagnetic field to some extent, the feasibility of wireless links traversing such structures, should be assessed. To this end, in the presented study two measurement campaigns were sorted out, in a context replicating real-life cases. During tests, the Long Range Wide Area Network (LoRaWAN) protocol was considered, since it has rather robust transmission capabilities where a low bitrate is needed. There, three combinations of transmission parameters were evaluated, specifically aiming at reproducing standard, emergency, and enhanced receiver sensitivity conditions. Test results proved the feasibility of performing at least a reliable single-hop wireless link traversing the underground tank. Moreover, the testes conditions can give valuable insight to practitioners in the field during the design phase of similar monitoring systems.

关键词： LoRaWAN IoT Safety Restricted Working Areas Health Monitoring Data Transmission

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Containerized Computer Vision applications on Edge Devices 7

Containerized Computer Vision Applications on Edge Devices

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7th ieee International Conference on Edge Computing and Communications (ieee EDGE) / ieee World Congress on Services (SERVICES)

作者： Alqaisi, Osamah I. Tosun, Ali Saman Korkmaz, Turgay Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA Univ North Carolina Pembroke Dept Math & Comp Sci Pembroke NC 28372 USA

ISBN: (纸本)9798350304831

The proliferation of IoT devices has led to various computer vision applications, where addressing bandwidth and latency challenges through edge nodes presents significant benefits. However, there are still existing gaps and a need for improvements to optimize IoT applications, especially in the field of computer vision, by overcoming limited resources and enhancing device performance. Addressing these challenges is essential to unlock the full potential of IoT applications in real-world scenarios. This paper evaluates the use of lightweight container technology for computer vision applications which using different algorithms, such as Haar Cascades, HOG and CNN with YOLO algorithm, on edge devices and provides a comprehensive comparison and analysis of different versions of computer vision applications in containers in terms of processing ability, and performance. It focuses on containerizing computer vision applications using Docker to achieve safe execution of multiple applications on these devices without interference and to enable flexibility, efficiency, portability, scalability, and isolation. The study also examines the resource usage, execution time, and receiving time of containerized computer vision applications. The research findings significantly advance our understanding of computer vision processing in IoT and edge computing, thereby opening up new avenues for real-time computing scenarios. These insights have the potential to drive transformative advancements in the field, enabling more efficient and accurate computer vision applications in IoT and paving the way for enhanced real-time decision-making, automation, and intelligent systems.

关键词： Edge Computing embedded Vision Container technology OpenCV Container Docker IoT

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LIGHTWEIGHT DISASTER SEMANTIC SEGMENTATION FOR UAV ON-DEVICE INTELLIGENCE

LIGHTWEIGHT DISASTER SEMANTIC SEGMENTATION FOR UAV ON-DEVICE...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Lee, Hanbin Kim, Gihwan Ha, Sangho Kim, Hyungshin Chungnam Natl Univ Dept Comp Sci & Engn Embedded Syst Lab ESLAB Daejeon South Korea

ISBN: (纸本)9798350360332;9798350360325

Unmanned aerial vehicles (UAVs) are being used for monitoring natural disasters. To promptly assess the severity of the disaster, it is advantageous to analyze the disaster scenes with the on-board computer in real time. Various AI segmentation models with high accuracy are developed mainly for offline processing. They require significant memory capacity and computational power. However, on-device AI has the challenge of compressing high-precision models due to the limited memory size and the small computation power. In this research, we develop a lightweight disaster semantic segmentation model for UAV on-device intelligence. From a simple FANet as our baseline, we apply various optimization, such as compact backbone, lightweight attention block, quantization, knowledge distillation, and post processing. With our optimized model, we can reduce 84.2% of the inference time while achieving 0.5% increase in accuracy compared to the baseline model.

关键词： Disaster monitoring on device AI semantic segmentation UAV neural network compression deep learning

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DRA: Ultra-Low Latency Network I/O for TSN embedded End-Systems 31

DRA: Ultra-Low Latency Network I/O for TSN Embedded End-Syst...

引用

31st ieee/ACM International symposium on Quality of Service, IWQoS 2023

作者： Li, Chenglong Li, Tao Li, Junnan Fu, Wenwen Wang, Baosheng College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798350399738

time-Sensitive Networking (TSN) is a promising open-source technique for hard real-time embedded fields such as industrial automation and autonomous driving. The embedded end-system deployed in TSN must guarantee deterministic latency and jitter for network I/O in above scenarios. Recent researchers focus on eliminating jitter but neglect to reduce latency. The network I/O latency is still too high to satisfy the dozen-microsecond requirements of latency-sensitive applications. We observed that (1) the data path from registers to external storage is actually the bottleneck for latency reduction, and (2) the serial processing of CPU and NIC can be further optimized. Therefore, we proposed DRA (Direct Register Access), a novel network I/O mechanism to achieve microsecond-level latency. DRA delivers whole packet data from the NIC directly into extended registers inside the CPU, avoiding the waste of time to move data between internal registers and external storage. Moreover, DRA promotes parallelization of CPU processing and NIC transfer to reduce latency. Considering the increasing popularity of RISC-V ISA in embedded systems, we prototype DRA using an open-source RISC-V core on FPGA and evaluate it under real-life application scenarios. Compared with existing mechanisms, experimental results demonstrate that DRA reduces the network I/O latency and jitter by at least 60% and 30%, achieving microsecond-level network I/O processing. © 2023 ieee.

关键词： Jitter

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Live Demonstration: A Reconfigurable, Energy efficient and High-frame-rate EKF-SLAM Accelerator Based SoC Design for Autonomous Mobile Robot applications

Live Demonstration: A Reconfigurable, Energy efficient and H...

引用

ieee International symposium on Circuits and Systems (ISCAS)

作者： Jiang, Dingcheng Liu, Bingqiang Wang, Jipeng Hu, Ao Zhao, Yequan Bao, Minjie Fan, Zhendong Shen, Zixuan Wang, Ke Wang, Chao Huazhong Univ Sci & Technol Sch Future Technol Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Opt & Elect Informat Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin Peoples R China

ISBN: (纸本)9798350330991;9798350331004

This demonstration shows a Extend Kalman Filter-Simultaneous Localization And Mapping (EKF-SLAM) accelerator based System On Chip (SoC) design for Autonomous Mobile Robots (AMR). The AMR platform consists of a multi-sensor system with a wheel encoder and LiDAR, and a ZYNQ-7000 FPGA based SoC featuring an EKF-SLAM hardware accelerator. This AMR system achieves real-time SLAM with significant energy efficient improvement against the state-of-the-art designs.

关键词： Machine design

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Micro-Architectural features as soft-error markers in embedded safety-critical systems: preliminary study 28

Micro-Architectural features as soft-error markers in embedd...

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28th ieee European Test symposium (ETS)

作者： Kasap, Deniz Carpegna, Alessio Savino, Alessandro Di Carlo, Stefano Politecn Torino Control & Comp Engn Dept Turin Italy Bilkent Univ Ankara Turkiye

ISBN: (纸本)9798350336344

Radiation-induced soft errors are one of the most challenging issues in Safety Critical real-time embedded System (SACRES) reliability, usually handled using different flavors of Double Modular Redundancy (DMR) techniques. This solution is becoming unaffordable due to the complexity of modern micro-processors in all domains. This paper addresses the promising field of using Artificial Intelligence (AI) based hardware detectors for soft errors. To create such cores and make them general enough to work with different software applications, microarchitectural attributes are a fascinating option as candidate fault detection features. Several processors already track these features through dedicated Performance Monitoring Unit (PMU). However, there is an open question to understand to what extent they are enough to detect faulty executions. Exploiting the capability of gem5 to simulate real computing systems, perform fault injection experiments, and profile microarchitectural attributes (i.e., gem5 Stats), this paper presents the results of a comprehensive analysis regarding the potential attributes to detect soft errors and the associated models that can be trained with these features.

关键词： reliability soft errors machine learning artificial neural networks soft error analysis

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Practical Implementation of Robust Human Activity Cataloguing with mmWave Radar

Practical Implementation of Robust Human Activity Cataloguin...

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2024 ieee Opportunity Research Scholars symposium, ORSS 2024

作者： Mii, Hiroki Shah, Swarna Lu, Bozhou Liu, Alan Lin, Yu-Tai Sundaresan, Karthikeyan Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9798350390698

Millimeter wave (mmWave) radar has recently become a popular choice of sensing technology for privacy-protecting health monitoring solutions. Though mmWave radar improves upon invasive alternatives like computer vision, the cost of privacy protection is sparser data. Many machine learning models designed to process this data are either computationally heavy or lack robustness and are impractical to deploy in real-time as required by applications such as physical therapy assistance, remote healthcare, and personal fitness assistance. Therefore, we aim to deploy a well-designed RF-driven Human Activity Cataloguer (RF-HAC) [1] for real-time operation. The model detects, categorizes, and counts repetitions of human exercises, and has a robust, accurate classification algorithm. To deploy this model into a real-time system, we focused on optimizing the data flow pipeline to stream data from the radar and process it to receive an output within seconds of the activity occurrence. Our system reduces the processing time from 3.1819 seconds to 1.1161 seconds, and our implemented data streaming pipeline produces a real-time output while maintaining a classification accuracy of 94%. © 2024 ieee.

关键词： Data transfer

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