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A FeFET-based Time-Domain Associative Memory for Multi-bit Similarity Computation

A FeFET-based Time-Domain Associative Memory for Multi-bit S...

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2024 Design, Automation and Test in Europe Conference and Exhibition, DATE 2024

作者： Huang, Qingrong Barkam, Hamza Errahmouni Yang, Zeyu Yang, Jianyi Kampfe, Thomas Ni, Kai Zhang, Grace Li Li, Bing Schlichtmann, Uif Imani, Mohsen Zhuo, Cheng Yin, Xunzhao Zhejiang University China University Of California Irvine United States ZJU-Hangzhou Global Scientific And Technological Innovation Center China Fraunhofer Ipms Germany University Of Notre Dame United States Technical University Of Darmstadt Germany Technical University Of Munich Germany Key Laboratory Of Collaborative Sensing And Autonomous Unmanned Systems Of Zhejiang Province China

ISBN: (纸本)9798350348590

The exponential growth of data across various domains of human society necessitates the rapid and efficient data processing. In many contemporary data-intensive applications, similarity computation (SC) is one of the most fundamental and indispensable operations. In recent years, In-memory computing (IMC) architectures have been designed to accelerate SC by reducing data movement costs, however, they encounter challenges with signal domain conversion, variation sensitivity, and limited precision. This paper proposes a ferroelectric FET (FeFET) based time-domain (TD) associative memory (AM) for energy efficient SC. Such TD design can convert its output (i.e., time interval) to digits with relatively simple sensing circuitry thus saves large amount of area and energy compared with conventional IMC designs that process analog voltage/current signals. The variable-capacitance (VC) delay chain structure in our design supports quantitative SC and enhances robustness against variations. Furthermore, by exploiting multi-domain ferroelctric FET (FeFET), our design is capable of performing SC on vectors with multi-bit element, enabling support for higher-precision algorithms. Simulation results show that the proposed TD-AM achieves 13.8x/1.47x energy saving of our design compared to CMOS/NVM based TD-IMC designs. Additionally, our design exhibits good robustness in monte carlo simulation with variation extracted from experimental measurements. Investigation on precision of hyperdimensional computing (HDC) show that higher element precision reduces the size of HDC model when considering to achieve same accuracy, indicating an improved efficiency. Benchmarkings against GPU demonstrate in general 2/3 orders of magnitude speedup/energy efficiency improvement of our design. Our proposed multi-bit TD-AM promises energy-efficient quantitative SC for diverse intensive data processing application, especially in energy-constrained scenarios. © 2024 EDAA.

关键词： Sensitivity analysis

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Distributed Tracing for Cascading Changes of Objects in the Kubernetes Control Plane 25

Distributed Tracing for Cascading Changes of Objects in the ...

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40th Annual acm Symposium on Applied computing, SAC 2025

作者： Ehira, Tomoyuki Kotani, Daisuke Okabe, Yasuo Kyoto University Kyoto Japan

ISBN: (纸本)9798400706295

Kubernetes is a container orchestration system that employs a declarative configuration management approach. In Kubernetes, each desired and actual state is represented in an "object", and multiple controllers autonomously monitor related objects and update their objects towards the desired state in the control plane. Because of this design, changes to one object cause changes to other objects in a chain, and the time to complete these cascading changes is an important metric for cluster operators to maintain application service quality on the cluster. However, there is no practical way to observe this kind of cascading change in detail, such as sequence of objects updated and the elapsed time of this update. Distributed tracing techniques, which are used in the microservice architecture to monitor application performance, are mainly based on RPC model that clearly identify request and response pairs, thus they are not directly applicable to the control plane of Kubernetes, where controllers just monitor objects and autonomously trigger changes to objects. In this paper, we propose a system that automatically traces changes to objects in the control plane. Our method adds one identifier, a Change Propagation ID (CPID), to the metadata of each object, and the controller that observes an object change propagates CPID in the object to the objects that the controller updates. When multiple changes need to be merged on an object, a new CPID is generated, and a pair of the original CPIDs and the new CPID is sent to the trace server for recording and analysis. Through evaluation, we confirmed that the proposed system can capture change propagation and its time, and the overhead of the proposed system is not significant. Copyright © 2025 held by the owner/author(s).

关键词： Distributed computer systems

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Capturing the Complexity of Cognitive computing systems: Co-Adaptation Theory for Individuals 21

Capturing the Complexity of Cognitive Computing Systems: Co-...

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Computers and People Research Conference (SIGMIS-CPR)

作者： Alahmad, Rasha Robert, Lionel P., Jr. Univ Michigan Sch Informat Ann Arbor MI 48109 USA

ISBN: (纸本)9781450384063

Cognitive computing systems (CCS) are the new generation of automated IT systems that mimic human cognitive capabilities. CCS reshape the interaction between humans and machines and challenge our traditional assumptions of technology use and adoption. This work introduces co-adaptation and defines it as the series of activities that a user and a system engage in simultaneously to make the system fit the user. Co-adaptation involves two types of adaptation: human adaptation and machine adaptation. Human adaptation refers to the user either changing their behavior to adjust to the technology or changing the technology to adjust to their use. Machine adaptation refers to the system adapting itself to fit users' needs. We use polynomial regression and response surface analysis to examine the impact of co-adaptation on individual performance. We add to previous work by offering a solid theoretical argument with supporting evidence that congruence between human adaptation and machine adaptation plays a critical role in determining the impact of technology use on individuals and their performance.

关键词： Technology Adaptation Co-adaptation Cognitive computing Artificial Intelligence IT identity

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A Threat Model Method for ICS Malware: the TRISIS case 21

A Threat Model Method for ICS Malware: the TRISIS case

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18th acm International Conference on computing Frontiers (CF)

作者： Mekdad, Yassine Bernieri, Giuseppe Conti, Mauro El Fergougui, Abdeslam Moulay Ismail Univ Meknes Lab Comp Networks & Syst Meknes Morocco Univ Padua Dept Math Padua Italy

ISBN: (纸本)9781450384049

Cyber-physical attacks against plants and Critical Infrastructures (CIs) are among the most significant concerns in the 21st century and can lead to devastating consequences. In particular, with the convergence between the Operational Technology (OT) network and the traditional IT network, malware threats for Industrial Control systems (ICSs) are gradually increasing. In these scenarios, we need to identify potential cyber threats by developing innovative modeling techniques. However, existing malware-based cyber threats modeling techniques are not fully designed for industrial environment. In this paper, we present a threat modeling framework for Industrial Control systems malware across two different levels: the Extraction Level and the Modeling Level. We evaluate the effectiveness of our model by analyzing the TRISIS cyber attack as a use case. A complex malware developed to cause operational disruption to industrial plants. Our solution outperforms existing malware threat modeling techniques for the ICS environment, and provides useful mitigation strategies to counter malicious activities.

关键词： Industrial Control System Malware analysis Diamond Model ICS Cyber Kill Chain

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vNV-Heap: An Ownership-Based Virtually Non-Volatile Heap for Embedded systems 25

vNV-Heap: An Ownership-Based Virtually Non-Volatile Heap for...

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proceedings of the 26th acm SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded systems

作者： Markus Elias Gerber Luis Gerhorst Ishwar Mudraje Kai Vogelgesang Thorsten Herfet Peter Wägemann Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Saarland University Saarbrücken Germany

ISBN: (纸本)9798400719219

The Internet of Batteryless Things might revolutionize our understanding of connected devices by harvesting required operational energy from the environment. These systems come with the system-software challenge that the intermittently powered IoT devices have to checkpoint their state in non-volatile memory to later resume with this state when sufficient energy is available. The scarce energy resources demand that only modified data is persisted before a power failure, which requires precise modification *** present vNV-Heap, the first ownership-based virtually Non-Volatile Heap for intermittently powered systems with guaranteed power-failure resilience. The heap exploits ownership systems, a zero-cost (i.e., compile-time) abstraction for example implemented by Rust, to track modifications and virtualize object persistence. To achieve power-failure resilience, our heap is designed and implemented to guarantee bounded operations by static program code analysis: For example, the heap allows for determining a worst-case energy consumption for the operation of persisting modified and currently volatile objects. The evaluation of our open-source implementation on an embedded hardware platform (i.e., ESP32-C3) shows that using our heap abstraction is more energy efficient than existing approaches while also providing runtime guarantees by static worst-case bounds.

关键词： Intermittent computing

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ASdb: A System for Classifying Owners of Autonomous systems 21

ASdb: A System for Classifying Owners of Autonomous Systems

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21s acm Internet measurement Conference (IMC)

作者： Ziv, Maya Izhikevich, Liz Ruth, Kimberly Izhikevich, Katherine Durumeric, Zakir Stanford Univ Stanford CA 94305 USA Univ Calif San Diego La Jolla CA USA

ISBN: (纸本)9781450391290

While Autonomous systems (ASes) are crucial for routing Internet traffic, organizations that own them are little understood. Regional Internet Registries (RIRs) inconsistently collect, release, and update basic AS organization information (e.g., website), and prior work provides only coarse-grained classification. Bootstrapping from RIR WHOIS data, we build ASdb, a system that uses data from established business intelligence databases and machine learning to accurately categorize ASes at scale. ASdb achieves 96% coverage of ASes, and 93% and 75% accuracy on 17 industry categories and 95 sub-categories, respectively. ASdb creates a more rich, accurate, comprehensive, and maintainable dataset cataloging AS-owning organizations. This system, and resulting dataset, will allow researchers to better understand who owns the Internet, and perform new forms of meaningful analysis and interpretation at scale.

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17th Workshop in Primary and Secondary computing Education, WiPSCE 2022

17th Workshop in Primary and Secondary Computing Education, ...

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17th Workshop in Primary and Secondary computing Education, WiPSCE 2022

ISBN: (纸本)9781450398541

The proceedings contain 29 papers. The topics discussed include: an integration of computational thinking and language arts: the contribution of digital storytelling to students’ learning;common patterns in block-based robot programs;common problems and effects of feedback on fun when programming ozobots in primary school;gender-dependent contribution, code and creativity in a virtual programming course;imagine yourself as a media and computer science teacher;video analysis of a teacher’s use of notional machines in an introductory high school electronic textile unit: a three-tier framework to capture notional machines in practice;grasping the concept of decentralized systems for instant messaging;quantifying the corona effect: how much the pandemic-induced switch from face-to-face to online teaching increased students self-efficacy – a practical report;teaching mathematical modeling with computing technology: presentation of a course based on evacuations;and comparing teachers’ and preservice teachers’ opinions on teaching methods in computer science education.

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Unveiling Energy Efficiency in Deep Learning: measurement, Prediction, and Scoring across Edge Devices 23

Unveiling Energy Efficiency in Deep Learning: Measurement, P...

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proceedings of the Eighth acm/IEEE Symposium on Edge computing

作者： Xiaolong Tu Anik Mallik Dawei Chen Kyungtae Han Onur Altintas Haoxin Wang Jiang Xie Georgia State University Atlanta Georgia USA The University of North Carolina at Charlotte Charlotte North Carolina USA Toyota Motor North America Mountain View California USA Toyota Motors North America Mountain View California USA

ISBN: (纸本)9798400701238

Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often overlooked, possibly due to a lack of sustainability mindset in the field and the absence of a holistic energy dataset. In this paper, we conduct a threefold study, including energy measurement, prediction, and efficiency scoring, with an objective to foster transparency in power and energy consumption within deep learning across various edge devices. Firstly, we present a detailed, first-of-its-kind measurement study that uncovers the energy consumption characteristics of on-device deep learning. This study results in the creation of three extensive energy datasets for edge devices, covering a wide range of kernels, state-of-the-art DNN models, and popular AI applications. Secondly, we design and implement the first kernel-level energy predictors for edge devices based on our kernel-level energy dataset. Evaluation results demonstrate the ability of our predictors to provide consistent and accurate energy estimations on unseen DNN models. Lastly, we introduce two scoring metrics, PCS and IECS, developed to convert complex power and energy consumption data of an edge device into an easily understandable manner for edge device end-users. We hope our work can help shift the mindset of both end-users and the research community towards sustainability in edge computing, a principle that drives our research. Find data, code, and more up-to-date information at https://***/DeepEn2023.

关键词： edge AI

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Global Energy Optimization Strategy Based on Delay Constraints in Edge computing Environment 21

Global Energy Optimization Strategy Based on Delay Constrain...

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24th International Conference on Modeling analysis and Simulation of Wireless and Mobile systems

作者： Tian, Xianzhong Zhou, Lu Xu, Ting Zhejiang Univ Technol Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9781450390774

Edge computing is one of the core technology of 5G networks. Edge computing deploys servers at the edge of the wireless access network, sinking cloud computing capabilities to the edge of the network, sharing the computing pressure of mobile users nearby, and improving the computing power of the entire network. Energy consumption is one of the important research issues of edge computing. At present, research on edge computing focuses on the energy consumption of terminal device, while little attention is paid to the energy consumption of edge servers. In this paper, considering the above two kinds of energy consumption, a global energy optimization strategy based on delay constraint in edge computing environment is proposed. Specifically, first, we use queuing theory to analyze the average delay of each terminal device and edge cloud processing computing tasks in the Internet of Things network, and the average delay of the entire system processing computing tasks. Secondly, we use the average delay as a constraint to establish a mathematical model for minimizing the total energy consumption of the device and the server. Then, we design a genetic algorithm-based offloading computation optimization algorithm to solve the above problems, so as to obtain the number of running servers in the edge cloud and the offload probability of IoT devices. Finally, the goal of minimizing the energy consumption of the overall system under the time delay constraint is achieved. The simulation experiment verifies the effectiveness of the energy optimization strategy.

关键词： SDN IoT edge computing computing offloading

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Exploring the Wearable and Embeddable Solutions for Biopotential Signal measurement: Dry and Non-Contact Technologies

Exploring the Wearable and Embeddable Solutions for Biopoten...

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2023 IEEE Biomedical Circuits and systems Conference, BioCAS 2023

作者： Klaic, Luka Stanesic, Antonio Culjak, Ivana Cifrek, Mario University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9798350300260

With a rising interest in wearable solutions and smart fitness gadgets, unobtrusive monitoring of physiological parameters is a topic gaining more and more interest. Being applied over clothes or embedded in the fabric, non-contact capacitive electrodes present a particularly interesting solution. However, in the context of digitalization of the healthcare industry, the question of the possibility of combining such a biomedical signal acquisition with artificial intelligence for postprocessing is posed. Instead of using biomedical signals generators, in this paper, capacitive electrodes with high input impedance are designed and used for real-life acquisition of electrocardiograms (ECGs), measured on thumbs over a layer of thin cotton gloves. These measurements are then used to analyze the influence of geometrical and fabric parameters on the quality of measured biomedical signals. For this purpose, instead of noise analysis, a novel approach was adopted: descriptive statistical features are used to evaluate the performance using four machine learning classifiers. The results have confirmed the possibility of classification and paved the way to further investigation of employing machine learning for capacitive biomonitoring. Furthermore, it is confirmed that tighter coupling, thicker fabric of lower dielectric permittivity, and larger detection surface area ensure better performance. © 2023 IEEE.

关键词： AIoT for biomedicine biomedical instrumentation capacitive electrodes ECG wearables

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