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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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Optimization Strategies for Digital Compute-in-Memory from Comparative Analysis with Systolic Array 5

Optimization Strategies for Digital Compute-in-Memory from C...

引用

5th IEEE international conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Li, Wantong Lee, Junmo Yu, Shimeng Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9798350332674

Compute-in-memory (CIM) is a promising solution to accelerate the extensive multiply-and-accumulate (MAC) operations in deep neural networks (DNNs). Although mixed-signal or analog CIM can achieve high energy efficiency, loss of inference accuracy due to noises and variations is its main drawback. Digital CIM (DCIM) has recently been proposed to achieve both high efficiency and high accuracy. In this work, we first analyze the fundamental properties of DCIM by comparing SRAM-based DCIM with TPU-like systolic array, another popular choice for DNN acceleration. With both designs evaluated in 28-nm process, we observe that DCIM is superior to systolic array in terms of area and power consumption. However, DCIM exhibits poor scalability of latency with increasing array sizes. To mitigate, we propose parallel-input bitwise-weight compute scheme for DCIM shorten its critical path, such that 25% improvement in operating frequency can be obtained with minimal overhead. We then study the systolic DCIM architecture to further enhance the scalability of DCIM. Compared to the compute cores of TPU, systolic DCIM is evaluated to provide 47% and 17% improvements in compute and energy efficiencies. © 2023 IEEE.

关键词： Deep neural networks

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A Multiple Measurement Vector-Based Deep Unfolded Network for One-bit DOA Estimation 5

A Multiple Measurement Vector-Based Deep Unfolded Network fo...

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5th international conference on Information Communication and Signal processing, ICICSP 2022

作者： Zhan, Mengchao Xi, Feng Chen, Shengyao Nanjing University of Science and Technology Department of Electronical Engineering Nanjing China

ISBN: (纸本)9781665485890

this paper introduces a new direction-of-arrival (DOA) estimation method for multi-snapshot narrowband signals. To reduce the system cost, we adopt one-bit compressed sensing in the process of sampling and quantization for analog signals. We propose a deep unfolded network (DUN) based on multiple measurement vectors (MMVs), known as the learned MMV-based binary iteration soft threshold (L-MMV-BIST) network, to estimate the DOAs from the one-bit measurements. this new DUN is designed by unfolding each update of the binary iterative soft threshold algorithm (ISTA) into a layer of a deep neural network, thus it has the ability to learn soft threshold and other iteration parameters adaptively. Our simulation results show that the L-MMV -BIST network can estimate DOA information from the one-bit measurements. In addition, this network outperforms traditional BIST algorithm in both computational complexity and recovery accuracy. © 2022 IEEE.

关键词： Deep neural networks

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Acoustic Bandwidth Extension by Audio Deep Residual U-Net 5

Acoustic Bandwidth Extension by Audio Deep Residual U-Net

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5th international conference on Information Communication and Signal processing, ICICSP 2022

作者： Ou, Linlin Chen, Yuanping Computer Network Information Center Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Science Beijing China

ISBN: (纸本)9781665485890

Mobile communication systems often rely on traditional channels with narrow bandwidth bottlenecks, reducing the audio quality. Due to the size of the network and the heterogeneity of devices, using high-quality audio codecs in real-world scenarios to transmit higher sample rate audio is difficult to practice. this paper proposes an approach by which communication nodes can extend the bandwidth of the band-limited input signal of a low sample rate codec, an audio synthesis neural network: ARUNet (Audio deep Residual U-Net), that integrates the advantage of residual learning, and U-Net is introduced to compensate for the high-frequency part of the live input audio clips. In addition, ARUNet has made outstanding achievements in various instrument solo data sets, with SNR close to 40dB and VGG distance close to 32. We explore the impact of our architectural design and demonstrate considerable improvements in terms of both perceptual and objective metrics. © 2022 IEEE.

关键词： Deep neural networks

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KI5GRob: Fusing Cloud Computing and AI for Scalable Robotic System in Production and Logistics 17th

KI5GRob: Fusing Cloud Computing and AI for Scalable Robotic ...

引用

17th international conference on Intelligent Autonomous Systems (IAS)

作者： Zhang, Yongzhou Soti, Gergely Hein, Bjorn Wurll, Christian Hsch Karlsruhe Univ Appl Sci D-76133 Karlsruhe Germany Karlsruhe Inst Technol D-76131 Karlsruhe Germany

ISBN: (纸本)9783031222153;9783031222160

Robotics and AI are essential components in current and future production scenarios. For example, in object handling, breakthroughs in AI have achieved revolutionary progress. As systems scale and the complexity of tasks increase, the use of deep learning in industrial robotics applications leads to specific challenges in terms of scalability, reliability, and safety. Some of these challenges can be overcome by utilizing cutting-edge technologies in cloud computing such as on-demand computing, massive parallelization, microservices, and the DevOps pipeline. With KI5GRob, we propose a novel approach to facilitate the development of robotic applications fusing cloud computing and AI. As example application for the proposed cloud approach, KI5GRob investigates machine learning methods for multi-modal sensor data processing and transfer learning to solve industrial robotic manipulation tasks. the goal of the project is to develop a microservice based software architecture that enables on-demand deployment of such learning-based methods as well as cloud-based motion planning and robot control. this paper gives a high-level overview of the goals and the ongoing research activities in KI5GRob. the first results about the overall hardware and software architecture are also presented.

关键词： Robotic manipulation Machine learning Multi-modal sensor fusion Transfer learning Cloud robotics Microservice Motion planning Robot control

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MPPT and ANN based Solar Tracking System using Photo Diode Sun sensor 5

MPPT and ANN based Solar Tracking System using Photo Diode S...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Sathish, R. Kumar, Vinod D. Jeyaganesan, C. Vinayaka Missions Res Fdn Deemed Be Univ Vinayaka Missions Kirupananda Variyar Engn Coll Elect & Elect Engn Salem Tamil Nadu India Vinayaka Missions Res Fdn Deemed Be Univ Vinayaka Missions Kirupananda Variyar Engn Coll Biomed Engn Salem Tamil Nadu India Vinayaka Missions Res Fdn Deemed Be Univ Vinayaka Missions Kirupananda Variyar Engn Coll Dept Elect & Elect Engn ME Power Syst Engn Salem Tamil Nadu India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

For solar energy generated by High Concentration Photovoltaic and Concentrating Solar Power technologies, precise tracking systems are needed. Artificial Neural Network (ANN) and Maximum Power Point Tracking (MPPT) based Closed-loop systems utilise data supplied by a sun sensor, which takes up direct and diffuse sunlight. Increased measurement noise at the sensor's output due to diffuse sunlight reduces the maximum attainable gains in the closed-loop controller, resulting in decreased tracking performance. A four-quadrant photodiode solar sensor may be improved by using infrared and linear polarising optical filters. Regardless of the kind of controller used in the solar tracker, these filters limit the influence of diffuse radiation and increase the effectiveness of sun tracking systems. this paper presents tracking experiments including solar trackers under both indoor and outdoor situations, using various methods, such as Maximum Power Point Tracking (MPPT), as well as artificial neural networks (ANN).

关键词： MPPT ANN Photo Diode Solar SEPIC converter

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Employing Satellite Imagery on Investigation of Convolutional Neural Network Image processing and Poverty Prediction Using Keras Sequential Model 5

Employing Satellite Imagery on Investigation of Convolutiona...

引用

5th IEEE international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Kumar, Voruganti Naresh Sonth, Mahesh V Mahvish, Arfa Koppula, Vijaya kumar Anuradha, B. Reddy, L Chandrasekhar CMR Technical Campus Department of CSE Telangana Hyderabad India CMR Technical Campus Department of ECE Telangana Hyderabad India CMR Technical Campus Department of IT Telangana Hyderabad India CMR College of Engineering & Technology Department of CSE Telangana Hyderabad India CMR College of Engineering & Technology Department of CSD Telangana Hyderabad India

ISBN: (纸本)9798350338287

the primary objective of the proposed model is that the most of the world's poorest individuals reside in areas where national domestic assessments are used to gather info on deficiency. It is difficult to acquire current and precise data because it takes a lot of assets to conduct these surveys. Due to advancements in computer vision and the widespread availability of abundant data sources such as satellite images captured during daylight and nocturnal lighting, a practical solution to the problem of data scarcity is now feasible. this study is going to expand on previous research by processing daytime satellite photos and nighttime lights employing machine learning techniques for the purpose predict, the distribution of poverty at the local level in countries utilizing new technology and modern data sources. Innovative and fascinating possibilities, such as the detailed classification of specific objects on a per-pixel basis, have become feasible due to the availability of aerial satellite data. this study demonstrates the efficacy of a convolutional neural network (CNN) in efficiently and accurately classifying individual pixels inside satellite imagery of a compact urban area. the broad segmentation is then refined by incorporating the expected detailed pixel classifications, enhancing the overall accuracy and speed of the classification process. Examined and assessed are the several architectural decisions made for the CNN architecture. the five different types of terrain, ground cover, roads, structures, and water are all physically categorized and assigned to the study area's land mass. the correctness of classification is compared with other per-pixel classification methods for contrast tests conducted on different terrain areas with a comparable number of categories. Convolutional Neural networks (CNNs) have demonstrated their efficacy in effectively addressing the task of segmenting and detecting objects in remote sensing data. this is clear from the compl

关键词： Satellite imagery

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Enhanced Secure Communication in Wireless sensor networks: Quantum Key Distribution with KIRSAN Gate

Enhanced Secure Communication in Wireless Sensor Networks: Q...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Kirankumar Manivannan Vivekanandan M Saranya G Kumaran K School of Electronics Engineering Vellore Institute of Technology India School of Computer Science and Engineering Vellore Institute of Technology India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

sensor nodes are integral to wireless sensor networks (WSNs) but their deployment across various locations exposes their communications to potential monitoring. Key challenges include message protection and node identification, necessitating efficient mechanisms for key distribution and management, especially in large-scale networks. Quantum cryptography, specifically quantum key distribution (QKD), offers a method to securely establish keys over short distances, leveraging principles like garbage output reduction and quantum communication for enhanced security. To address these issues, reversible logic gates, such as the newly developed

关键词： Wireless communication Wireless sensor networks Protocols Logic gates Quantum key distribution Security Quantum cryptography Protection Monitoring Quantum communication

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Research on Fault Tolerant Mechanisms in Wireless sensor networks 23

Research on Fault Tolerant Mechanisms in Wireless Sensor Net...

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Proceedings of the 2023 5th international conference on Internet of things, Automation and Artificial Intelligence

作者： Pengfei Ma Xiangdong Li Yucheng Wei Qilang Jia Boran Xu Systems Engineering Research Institute Shipbuilding Corporation Limited China

ISBN: (纸本)9798400716485

Data is an important foundation of artificial intelligence. sensors are an important source of data. Wireless sensor networks need to have fault-tolerant capabilities, especially in large-scale wireless sensor networks. Fault tolerance technology is committed to maintaining the normal operation of the network in the face of errors, faults, or abnormal situations, providing continuous data support for artificial intelligence systems. this article studies the fault tolerance of wireless sensor networks through topology. Redundancy and dynamic threshold setting are indispensable elements in the fault-tolerant design of wireless sensor networks, which together contribute to achieving stable operation and high availability of the network in complex environments.

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A sensorless MPPT Structure for Low Cost Solar Energy Harvesting in Wireless sensor networks

A Sensorless MPPT Structure for Low Cost Solar Energy Harves...

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IEEE international conference on Circuits and Systems (ICCS)

作者： Lijuan Fan Shunren Hu Wei Liu School of Electrical and Electronic Engineering Chongqing University of Technology

Maximum power point tracking (MPPT) is necessary for efficient solar energy harvesting. In the context of wireless sensor networks (WSNs), MPPT often makes use of sensors to aid in calculating the maximum power point voltage of solar panel. However, existing methods are susceptible to changes in environmental conditions and system parameters and are unable to handle the complex and dynamic application scenarios for WSNs. In this paper, a sensorless MPPT structure is proposed which uses the solar panel for energy harvesting itself as the sensor and employs simple circuit structure and control flow to execute MPPT. Experimental results show that in terms of energy harvesting efficiency, the proposed structure performs better than existing ones.

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Brains Over Brawn: Small AI Labs in the Age of Datacenter-Scale Compute 5th

Brains Over Brawn: Small AI Labs in the Age of Datacente...

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5th international conference on Deep Learning theory and Applications, DeLTA 2024

作者： Put, Jeroen Michiels, Nick Vanherle, Bram Zoomers, Brent Flanders Make Expertise Centre for Digital Media Hasselt University Diepenbeek Belgium SB PhD Fellow at FWO Diepenbeek Belgium

ISBN: (纸本)9783031667046

the prevailing trend towards large models that demand extensive computational resources threatens to marginalize smaller research labs, constraining innovation and diversity in the field. this position paper advocates for a strategic pivot of small institutions to research directions that are computationally economical, specifically through a modular approach inspired by neurobiological mechanisms. We argue for a balanced approach that draws inspiration from the brain’s energy-efficient processing and specialized structures, yet is liberated from the evolutionary constraints of biological growth. By focusing on modular architectures that mimic the brain’s specialization and adaptability, we can strive to keep energy consumption within reasonable bounds. Recent research into forward-only training algorithms has opened up concrete avenues to include such modules into existing networks. this approach not only aligns with the imperative to make AI research more sustainable and inclusive but also leverages the brain’s proven strategies for efficient computation. We posit that there exists a middle ground between the brain and datacenter-scale models that eschews the need for excessive computational power, fostering an environment where innovation is driven by ingenuity rather than computational capacity. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

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