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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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Synthesis of Compact Flow-based computing Circuits from Boolean Expressions 24

Synthesis of Compact Flow-based Computing Circuits from Bool...

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61st ACM/ieee Design Automation conference, DAC 2024

作者： Thijssen, Sven Rashed, Muhammad Rashedul Haq Jha, Sumit Ewetz, Rickard University of Central Florida OrlandoFL United States Florida International University MiamiFL United States

ISBN: (纸本)9798400706011

Processing in-memory has the potential to accelerate high-data-rate applications beyond the limits of modern hardware. Flow-based computing is a computing paradigm for executing Boolean logic within nanoscale memory arrays by leveraging the natural flow of electric current. Previous approaches of mapping Boolean logic onto flow-based computing circuits have been constrained by their reliance on binary decision diagrams (BDDs), which translates into high area overhead. In this paper, we introduce a novel framework called FACTOR for mapping logic functions into dense flow-based computing circuits. The proposed methodology introduces Boolean connectivity graphs (BCGs) as a more versatile representation, capable of producing smaller crossbar circuits. The framework constructs concise BCGs using factorization and expression trees. Next, the BCGs are modified to be amenable for mapping to crossbar hardware. We also propose a time multiplexing strategy for sharing hardware between different Boolean functions. Compared with the state-of-the-art approach, the experimental evaluation using 14 circuits demonstrates that FACTOR reduces area, speed, and energy with 80%, 2%, and 12%, respectively, compared with the state-of-the-art synthesis method for flow-based computing. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Boolean functions

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Energy-Efficient Task Scheduling based on Whale Optimization Algorithm Cyber-Physical systems 2

Energy-Efficient Task Scheduling based on Whale Optimization...

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2nd ieee international conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Al-Jawahry, Hassan M. Shankar, Smitha Sharath Chythanya, Kanegonda Ravi Maharajan, K. Swathi, Akuthota The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Sr University School of Computer Science and Artificial Intelligence Warangal India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Krishnankoil India Cvr College of Engineering Department of CSE Telangana India

ISBN: (纸本)9798350317558

The extensive application of Cyber-Physical systems (CPS) requires efficient optimization of computational units and physical plants. Task scheduling (TS) is critical to improving resource utilization and system efficacy within CPS. Standard task schedulers in embedded real-time techniques fall short of achieving CPS performance criteria due to task diversity and system heterogeneity. This research proposes a novel task scheduling technique called Whale Optimization Algorithm based Energy-Efficient TS (WOA-EETS) that is specifically designed for the CPS context. WOA-EETSS involves assigning number of autonomous tasks to various resources during the TS process. The objective function focuses on reducing task completion time and optimizing resource use. A series of simulations were run to demonstrate WOA-EETSS' improved performance. The comparison analysis demonstrated that the WOA-EETSS system performed better across a variety of parameters. The proposed approach attains the better performance in Task Execution (ms) of 347ms, Convergence Rate of 511ms and Average Load Balancing Ratio (%) of 69.91% from 100 iterations. © 2024 ieee.

关键词： Cyber-Physical systems Energy Efficient Task Diversity Task Scheduling Whale Optimization Algorithm

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A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons

A Comparison of Temporal Encoders for Neuromorphic Keyword S...

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international Joint conference on Neural Networks (IJCNN)

作者： Nilsson, Mattias Pina, Ton Juny Khacef, Lyes Liwicki, Foteini Chicca, Elisabetta Sandin, Fredrik Lulea Univ Technol Embedded Intelligent Syst Lab EISLAB S-97187 Lulea Sweden Univ Groningen Zernike Inst Adv Mat Bioinspired Circuits & Syst BICS Lab NL-9747 AG Groningen Netherlands Univ Groningen Groningen Cognit Syst & Mat Ctr CogniGron NL-9747 AG Groningen Netherlands

ISBN: (纸本)9781665488679

With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a "wake-up" mechanism for subsequent computationally expensive speech recognition. One promising approach is the use of neuromorphic sensors and spiking neural networks (SNNs) implemented in neuromorphic processors for sparse event-driven sensing. However, this requires resource-efficient SNN mechanisms for temporal encoding, which need to consider that these systems process information in a streaming manner, with physical time being an intrinsic property of their operation. In this work, two candidate neurocomputational elements for temporal encoding and feature extraction in SNNs described in recent literature-the spiking time-difference encoder (TDE) and disynaptic excitatory-inhibitory (E-I) elements-are comparatively investigated in a keyword-spotting task on formants computed from spoken digits in the TIDIGITS dataset. While both encoders improve performance over direct classification of the formant features in the training data, enabling a complete binary classification with a logistic regression model, they show no clear improvements on the test set. Resource-efficient keyword spotting applications may benefit from the use of these encoders, but further work on methods for learning the time constants and weights is required to investigate their full potential.

关键词： Neuromorphic computing edge intelligence keyword spotting temporal code neural heterogeneity

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Single Chip Traction Inverter System using High Performance and real-time AM263 MCU

Single Chip Traction Inverter System using High Performance ...

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ieee international conference on Consumer Electronics (ICCE)

作者： Mody, Mihir Murphy, Sean Shankar, Prithvi Kumar, Vc Farooqui, Mohammad Asif Shah, Malay Sangani, Nikhil Pratti, Anita Ghotgalkar, Shailesh Chellappan, Sriraj Sm, Stalin Nagalikar, Shivasharan Rosales, Randy Rangan, Rohit Dixit, Tanu Ming, Wei Gangadhar, Pratheesh Johndale, Prasad Karaiyan, Sanjeev Golla, Ajay Texas Instruments Inc Embedded Proc Dallas TX 75243 USA

ISBN: (纸本)9781665491303

A traction inverter converts battery energy into power that controls motor torque and speed, giving it the most influence over an EV's range, performance and driving experience. The typical system consists of safety MCU, DSP for real time motor control processing along with hardware resolver for position sensing. The speed for traction motor is pushed to higher e.g. 10-30K rpm to reduce cost by miniaturizing motor and passive components, which necessitates the real time control loop performance in uSec range. The paper proposed a single chip traction Inverter System using Texas Instruments (TI) AM263 MCU, which is designed for real-time and high performance for optimal system cost for traction inverter. The paper proposes innovative techniques namely optimized Field Oriented Control (FOC) control algorithm with custom math algorithms, Software based resolver and double PWM update for better control performance. The overall solution is prototyped and demoed on hardware design and measured for overall performance. The real-time performance of control loop is 3.9 usec, which is less than 1% for overall processing power for 10K rpm. The proposed single chip solution has 99% headroom for running AUTOSAR operating system and customer's applications for differentiation.

关键词： Traction inverter MCU DSP AM263x AUTOSAR control loop Resolver FOC

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real-time detection of road hazards for autonomous vehicle systems 6

Real-time detection of road hazards for autonomous vehicle s...

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6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Szabo, Mate Andras Keszler, Anita Tizedes, Laszlo HUN-REN SZTAKI Machine Perception Research Lab. Budapest Hungary

ISBN: (纸本)9798331506520

Automated detection of road hazards such as speed bumps, has become an important area of research due to its potential to improve road safety in autonomous driving. Various techniques have been introduced to detect these hazards using camera vision and artificial intelligence-based image processing methods. However, estimating their distance is still challenging. To address this problem and to satisfy the requirement for real-time on-board data processing, the proposed system has the following properties: (1) high-accuracy road hazard detection by analyzing mono-images and videos with a re-trained YOLO neural network;(2) precise distance measurement utilizing a LiDAR;and (3) efficient local data processing using ROS, implemented on an NVIDIA Jetson AGX Xavier. An important contribution of this paper is introducing multiple classes of road hazards when training the network, instead of only focusing on speed bumps and potholes. Furthermore we have analyzed different LiDAR technologies (standard rotating and non-repetitive circular scanning) to evaluate and compare their precision and to demonstrate that our method can be successfully applied regardless of the scanning pattern of the LiDAR. © 2025 ieee.

关键词： Gluing

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Cloud Revolution in Manufacturing: Exploring Benefits, applications, and Challenges in the Era of Digital Transformation

Cloud Revolution in Manufacturing: Exploring Benefits, Appli...

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2024 ASU international conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Umar, Umar Sunusi Rana, Muhammad Ehsan School of Computing Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia

ISBN: (纸本)9798350372229

This study investigates the profound impact of cloud computing on the manufacturing industry, accentuating its potential benefits and versatile applications. The study meticulously explores adoption trends, backed by real-world case studies, and evaluates the appropriateness of diverse cloud service and deployment models within manufacturing. Key cloud features, including scalability, availability, automation, efficiency, and collaboration, are dissected for their empowerment in the manufacturing domain. Furthermore, a critical review of leading cloud service providers, namely AWS, Azure, and GCP, elucidates their features geared towards enhancing performance, fostering innovation, and driving transformation in manufacturing. The research advocates for AWS as the prime recommendation due to its extensive service offerings and dominance in machine learning, a pivotal aspect of manufacturing evolution. Concurrently, the challenges intrinsic to cloud adoption in manufacturing, particularly concerning security, privacy, and management complexities, are scrutinized. Despite these challenges, the study asserts that cloud computing stands as a transformative force, steering the modernization of manufacturing operations toward increased efficiency and innovation. © 2024 ieee.

关键词： Cloud Adoption Cloud computing Digital transformation Manufacturing Challenges Manufacturing Industry

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ieee international conference on Advances in Electronics, Communication, computing and Intelligent Information systems, ICAECIS 2023 - Proceedings

IEEE International Conference on Advances in Electronics, Co...

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1st ieee international conference on Advances in Electronics, Communication, computing and Intelligent Information systems, ICAECIS 2023

ISBN: (纸本)9798350348057

The proceedings contain 127 papers. The topics discussed include: automated Bengali abusive text classification: using deep learning techniques;bio algorithms for resource optimization and analysis of data;prediction of rheumatoid arthritis susceptibility using gene mutation rate;a survey paper on emerging techniques used to translate audio or text to sign language;a novel development of blockchain based messaging application;deep machine learning based usage pattern and application classifier in network traffic for anomaly detection;analyzing paralinguistic information from human speech and its applications in medicine;strategic placement of electric vehicle charging stations using grading algorithm;malware detection in android applications using machine learning;the impact of online reviews on product perception and purchase intention;design and implementation of smart classroom using cisco packet tracer;intrusion detection in networks using gradient boosting;real-time lane detection and departure caution gadget for automobiles based on Raspberry pi;development of a Bengali speech-based emotion analysis system;large vocabulary continuous speech recognition system for Marathi;and low-cost smart glasses for people with visual impairments.

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Clustering-TinyPointNet for Fast Large-scale Point Cloud Semantic Segmentation

Clustering-TinyPointNet for Fast Large-scale Point Cloud Sem...

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2023 international conference on Digital Image computing: Techniques and applications, DICTA 2023

作者： Wang, Zhenglin Walsh, Kerry Sabrina, Fariza Piyathilaka, Lasitha Lin, Yufeng Central Queensland University School of Engineering and Technology Rockhampton Australia Institute for Future Farming Systems Central Queensland University Rockhampton Australia

ISBN: (纸本)9798350382204

Efficient processing of massive point cloud datasets is crucial for achieving fast semantic segmentation in various applications. While PointNet++ has demonstrated excellent performance in point cloud segmentation, its processing speed may not meet the requirements of real-time applications. This paper investigates the PointNet++ approach and proposes a novel deep learning architecture, termed Clustering-TinyPointNet, which aims to perform coarse-to-fine point cloud data segmentation using a combination of a fast K-means++ clustering algorithm and a lightweight neural network. The Clustering-TinyPointNet method is derived from PointNet++ by reducing the total number of layers from 79 to 55 and the number of learnable parameters from 892.8K to 638.2K. As a result, the proposed method achieves a reduction in running time by over 30% while maintaining comparable segmentation accuracy. This advantage positions Clustering-TinyPointNet as a promising solution for efficient point cloud semantic segmentation in real-time applications. © 2023 ieee.

关键词： K-means clustering

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Efficient Compression Technique for Large Size Binary Sparse Matrix Using Modified Run Length Encoding for Memory Constraint embedded systems

Efficient Compression Technique for Large Size Binary Sparse...

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2023 ieee international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2023

作者： Nikhil, G.S. Pandey, Puneet Hota, Manjit Goel, Manish Samsung Semiconductors India R and D Sensor Algo and SW Team Bengaluru India Samsung Semiconductors India R and D Sensor Team Bengaluru India

ISBN: (纸本)9781665462631

Binary Sparse Matrix storage is one of the critical problems in embedded system applications. Storing these matrices in the memory efficiently is important. Magnitude of increase in matrix size also has significant impact on the memory requirement. Sometimes, it might not be possible to accommodate the data due to memory constraints. In this work, we have analyzed some of state-of-the-art methods deployed for storing these matrices in the system on-chip memory and we have demonstrated the shortcomings of each. Thus, we propose a modified version of run length encoding which scales aptly with matrix size and sparsity density of the binary sparse matrix. Through simulations we have shown that the proposed method performs better than many state-of-the-art algorithms. © 2023 ieee.

关键词： embedded systems

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Exploiting Temporal Context for Tiny Object Detection

Exploiting Temporal Context for Tiny Object Detection

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23rd ieee/CVF Winter conference on applications of Computer Vision (WACV)

作者： Corsel, Christof W. van Lier, Michel Kampmeijer, Leo Boehrer, Nicolas Bakker, Erwin M. Leiden Univ LIACS Leiden Netherlands TNO Intelligent Imaging The Hague Netherlands

ISBN: (纸本)9798350320565

In surveillance applications, the detection of tiny, lowresolution objects remains a challenging task. Most deep learning object detection methods rely on appearance features extracted from still images and struggle to accurately detect tiny objects. In this paper, we address the problem of tiny object detection for real-time surveillance applications, by exploiting the temporal context available in video sequences recorded from static cameras. We present a spatiotemporal deep learning model based on YOLOv5 that exploits temporal context by processing sequences of frames at once. The model drastically improves the identification of tiny moving objects in the aerial surveillance and person detection domains, without degrading the detection of stationary objects. Additionally, a two-stream architecture that uses frame-difference as explicit motion information was proposed, further improving the detection of moving objects down to 4 x 4 pixels in size. Our approaches outperform previous work on the public WPAFB WAMI dataset, as well as surpassing previous work on an embedded NVIDIA Jetson Nano deployment in both accuracy and inference speed. We conclude that the addition of temporal context to deep learning object detectors is an effective approach to drastically improve the detection of tiny moving objects in static videos.

关键词： Deep learning Surveillance Video sequences Object detection Detectors Feature extraction real-time systems

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