Literatura académica sobre el tema "Embedded AI"
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Artículos de revistas sobre el tema "Embedded AI"
Hammer, Jürgen. "Berührungsängste mit "Embedded AI"?" MTZ - Motortechnische Zeitschrift 82, n.º 4 (12 de marzo de 2021): 70. http://dx.doi.org/10.1007/s35146-021-0653-1.
Texto completoAshfaq, Zarlish, Rafia Mumtaz, Abdur Rafay, Syed Mohammad Hassan Zaidi, Hadia Saleem, Sadaf Mumtaz, Adnan Shahid, Eli De Poorter y Ingrid Moerman. "Embedded AI-Based Digi-Healthcare". Applied Sciences 12, n.º 1 (5 de enero de 2022): 519. http://dx.doi.org/10.3390/app12010519.
Texto completoOrtmeyer, Cliff. "AI Options for Embedded Systems". New Electronics 52, n.º 3 (12 de febrero de 2019): 26–27. http://dx.doi.org/10.12968/s0047-9624(22)60909-x.
Texto completoYoon, Young Hyun, Dong Hyun Hwang, Jun Hyeok Yang y Seung Eun Lee. "Intellino: Processor for Embedded Artificial Intelligence". Electronics 9, n.º 7 (18 de julio de 2020): 1169. http://dx.doi.org/10.3390/electronics9071169.
Texto completoHammer, Jürgen. "A Reluctance to Use Embedded AI?" MTZ worldwide 82, n.º 4 (12 de marzo de 2021): 68. http://dx.doi.org/10.1007/s38313-021-0636-0.
Texto completoBastani, F. B. y I. R. Chen. "The reliability of embedded AI systems". IEEE Expert 8, n.º 2 (abril de 1993): 72–78. http://dx.doi.org/10.1109/64.207431.
Texto completoTyler, Neil. "DSPs Target Embedded Vision and AI". New Electronics 54, n.º 7 (27 de abril de 2021): 6. http://dx.doi.org/10.12968/s0047-9624(22)60260-8.
Texto completoMcLennan, Stuart, Amelia Fiske, Leo Anthony Celi, Ruth Müller, Jan Harder, Konstantin Ritt, Sami Haddadin y Alena Buyx. "An embedded ethics approach for AI development". Nature Machine Intelligence 2, n.º 9 (31 de julio de 2020): 488–90. http://dx.doi.org/10.1038/s42256-020-0214-1.
Texto completoCho, Sungjae, Yoonsu Kim, Jaewoong Jang y Inseok Hwang. "AI-to-Human Actuation". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, n.º 1 (27 de marzo de 2022): 1–32. http://dx.doi.org/10.1145/3580812.
Texto completoDini, Pierpaolo, Lorenzo Diana, Abdussalam Elhanashi y Sergio Saponara. "Overview of AI-Models and Tools in Embedded IIoT Applications". Electronics 13, n.º 12 (13 de junio de 2024): 2322. http://dx.doi.org/10.3390/electronics13122322.
Texto completoTesis sobre el tema "Embedded AI"
Chollet, Nicolas. "Embedded-AI-enabled semantic IoT platform for agroecology". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG078.
Texto completoModern agriculture requires a profound transformation to address the challenges of sustainable development while qualitatively and quantitatively feeding the growing global population. In this light, farmers are adopting "Smart Farming" also called precision agriculture. It is an agricultural method that leverages technology to enhance the efficiency, productivity, and sustainability of agricultural production. This approach encompasses the use of sensors, the Internet of Things (IoT), Artificial Intelligence (AI), data analysis, robotics, and various other digital tools optimizing aspects such as soil management, irrigation, pest control, and livestock management. The goal is to increase production while reducing resource consumption, minimizing waste, and improving product quality. However, despite its benefits and successful deployment in various projects, smart agriculture encounters limitations, especially within the context of IoT. Firstly, platforms must be capable of perceiving data in the environment, interpreting it, and making decisions to assist in farm management. The volume, variety, and velocity of those data, combined with a wide diversity of objects and the advent of AI embedded in sensors, make communication challenging on wireless agricultural networks. Secondly, research tends to focus on projects addressing the issues of non-sustainable conventional agriculture, and projects related to small-scale farms focused on agroecology are rare. In this context, this thesis explores the creation of an IoT platform comprised of a network of semantic smart sensors, aiming to guide farmers in transitioning and managing their farm sustainably while minimizing human intervention
Biswas, Avishek Ph D. Massachusetts Institute of Technology. "Energy-efficient smart embedded memory design for IoT and AI". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117831.
Texto completoThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted student-submitted PDF version of thesis.
Includes bibliographical references (pages 137-146).
Static Random Access Memory (SRAM) continues to be the embedded memory of choice for modern System-on-a-Chip (SoC) applications, thanks to aggressive CMOS scaling, which keeps on providing higher storage density per unit silicon area. As memory sizes continue to grow, increased bit-cell variation limits the supply voltage (Vdd) scaling of the memory. Furthermore, larger memories lead to data transfer over longer distances on chip, which leads to increased power dissipation. In the era of the Internet-of-Things (IoT) and Artificial Intelligence (AI), memory bandwidth and power consumption are often the main bottlenecks for SoC solutions. Therefore, in addition to Vdd scaling, this thesis also explores leveraging data properties and application-specfic features to design more tailored and "smarter" memories. First, a 128Kb 6T bit-cell based SRAM is designed in a modern 28nm FDSOI process. Dynamic forward body-biasing (DFBB) is used to improve the write operation, and reduce the minimum Vdd to 0.34V, even with 6T bit-cells. A new layout technique is proposed for the array, to reduce the energy overhead of DFBB and decrease the unwanted bit-line switching for un-selected columns in the SRAM, providing dynamic energy savings. The 6T SRAM also uses data prediction in its read path, to provide upto 36% further dynamic energy savings, with correct predictions. The second part of this thesis, explores in-memory computation for reducing data movement and increasing memory bandwidth, in data-intensive machine learning applications. A 16Kb SRAM with embedded dot-product computation capability, is designed for binary-weight neural networks. Highly parallel analog processing in- side the memory array, provided better energy-efficiency than conventional digital implementations. With our variation-tolerant architecture and support of multi-bit resolutions for inputs/outputs, > 98% classication accuracy was demonstrated on the MNIST dataset, for the handwritten digit recognition application. In the last part of the thesis, variation-tolerant read-sensing architectures are explored for future non-volatile resistive memories, e.g. STT-RAM.
by Avishek Biswas.
Ph. D.
Bartoli, Giacomo. "Edge AI: Deep Learning techniques for Computer Vision applied to embedded systems". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16820/.
Texto completoRoyles, Christopher Andrew. "Intelligent presentation and tailoring of online legal information". Thesis, University of Liverpool, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343616.
Texto completoMAZZIA, VITTORIO. "Machine Learning Algorithms and their Embedded Implementation for Service Robotics Applications". Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2968456.
Texto completoMOCERINO, LUCA. "Hardware-Aware Cross-Layer Optimizations of Deep Neural Networks for Embedded Systems". Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972558.
Texto completoFredriksson, Tomas y Rickard Svensson. "Analysis of machine learning for human motion pattern recognition on embedded devices". Thesis, KTH, Mekatronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-246087.
Texto completoAntalet uppkopplade enheter ökar och det senaste uppsvinget av ar-tificiell intelligens driver forskningen framåt till att kombinera de två teknologierna för att både förbättra existerande produkter och utveckla nya. Maskininlärning är traditionellt sett implementerat på kraftfulla system så därför undersöker den här masteruppsatsen potentialen i att utvidga maskininlärning till att köras på inbyggda system. Den här undersökningen av existerande maskinlärningsalgoritmer, implemen-terade på begränsad hårdvara, har utförts med fokus på att klassificera grundläggande mänskliga rörelser. Tidigare forskning och implemen-tation visar på att det ska vara möjligt med vissa begränsningar. Den här uppsatsen vill svara på vilken hårvarubegränsning som påverkar klassificering mest samt vilken klassificeringsgrad systemet kan nå på den begränsande hårdvaran. Testerna inkluderade mänsklig rörelsedata från ett existerande dataset och inkluderade fyra olika maskininlärningsalgoritmer på tre olika system. SVM presterade bäst i jämförelse med CART, Random Forest och AdaBoost. Den nådde en klassifikationsgrad på 84,69% på de sex inkluderade rörelsetyperna med en klassifikationstid på 16,88 ms per klassificering på en Cortex M processor. Detta är samma klassifikations-grad som en vanlig persondator når med betydligt mer beräknings-resurserresurser. Andra hårdvaru- och algoritm-kombinationer visar en liten minskning i klassificeringsgrad och ökning i klassificeringstid. Slutsatser kan dras att minnet på det inbyggda systemet påverkar vilka algoritmer som kunde köras samt komplexiteten i datan som kunde extraheras i form av attribut (features). Processeringshastighet påverkar mest klassificeringstid. Slutligen är prestandan för maskininlärningsy-stemet bunden till typen av data som ska klassificeras, vilket betyder att olika uppsättningar av algoritmer och hårdvara påverkar prestandan olika beroende på användningsområde.
Hasanzadeh, Mujtaba y Alexandra Hengl. "Real-Time Pupillary Analysis By An Intelligent Embedded System". Thesis, Mälardalens högskola, Inbyggda system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44352.
Texto completoTUVERI, GIUSEPPE. "Integrated support for Adaptivity and Fault-tolerance in MPSoCs". Doctoral thesis, Università degli Studi di Cagliari, 2013. http://hdl.handle.net/11584/266097.
Texto completoAntonini, Mattia. "From Edge Computing to Edge Intelligence: exploring novel design approaches to intelligent IoT applications". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/308630.
Texto completoLibros sobre el tema "Embedded AI"
Wang, Cliff, S. S. Iyengar y Kun Sun, eds. AI Embedded Assurance for Cyber Systems. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42637-7.
Texto completoNi de ai qing, wo zai li mian: Embedded in your love. Taibei Shi: Chun tian chu ban guo ji wen hua you xian gong si, 2008.
Buscar texto completoAI at the Edge: Solving Real World Problems with Embedded Machine Learning. O'Reilly Media, Incorporated, 2023.
Buscar texto completoDrage, Eleanor y Kerry McInerney, eds. The Good Robot. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9781350399990.
Texto completoGouzouasis, Peter y Danny Bakan. Arts-Based Educational Research in Community Music. Editado por Brydie-Leigh Bartleet y Lee Higgins. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190219505.013.17.
Texto completoBlömer, Michael, Stefan Riedel, Miguel John Versluys y Engelbert Winter, eds. Common Dwelling Place of all the Gods. Commagene in its Local, Regional and Global Hellenistic Context. Franz Steiner Verlag, 2021. http://dx.doi.org/10.25162/9783515129268.
Texto completoCapítulos de libros sobre el tema "Embedded AI"
Bräunl, Thomas. "AI Concepts". En Embedded Robotics, 403–19. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-0804-9_18.
Texto completoFeyock, Stefan y James L. Rogers. "Embedded AI for Structural Optimization". En Computational Mechanics ’88, 1281–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-61381-4_340.
Texto completoVermesan, Ovidiu y Marcello Coppola. "Edge AI Platforms for Predictive Maintenance in Industrial Applications". En Embedded Artificial Intelligence, 89–104. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-9.
Texto completoYoo, Hoi-Jun. "Mobile Embedded DNN and AI SoCs". En Low Power Circuit Design Using Advanced CMOS Technology, 287–361. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338772-4.
Texto completoMateu, Loreto, Johannes Leugering, Roland Müller, Yogesh Patil, Maen Mallah, Marco Breiling y Ferdinand Pscheidl. "Tools and Methodologies for Edge-AI Mixed-Signal Inference Accelerators". En Embedded Artificial Intelligence, 25–34. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-3.
Texto completoMiro-Panades, Ivan, Inna Kucher, Vincent Lorrain y Alexandre Valentian. "Meeting the Latency and Energy Constraints on Timing-critical Edge-AI Systems". En Embedded Artificial Intelligence, 61–67. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-6.
Texto completoGu, Yichi. "AI Embedded Transparent Health and Medicine System". En Advances in Intelligent Systems and Computing, 18–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32520-6_2.
Texto completoQu, Zhe, Rui Duan, Yao Liu y Zhuo Lu. "Federated Learning for IoT Applications, Attacks and Defense Methods". En AI Embedded Assurance for Cyber Systems, 161–81. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42637-7_9.
Texto completoLosavio, Michael. "Forensic Proof and Criminal Liability for Development, Distribution and Use of Artificial Intelligence". En AI Embedded Assurance for Cyber Systems, 37–48. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42637-7_3.
Texto completoKumar, K. J. Latesh, Yashas Hariprasad, K. S. Ramesh y Naveen Kumar Chaudhary. "AI Powered Correlation Technique to Detect Virtual Machine Attacks in Private Cloud Environment". En AI Embedded Assurance for Cyber Systems, 183–99. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42637-7_10.
Texto completoActas de conferencias sobre el tema "Embedded AI"
Brandalero, Marcelo, Muhammad Ali, Laurens Le Jeune, Hector Gerardo Munoz Hernandez, Mitko Veleski, Bruno da Silva, Jan Lemeire et al. "AITIA: Embedded AI Techniques for Embedded Industrial Applications". En 2020 International Conference on Omni-layer Intelligent Systems (COINS). IEEE, 2020. http://dx.doi.org/10.1109/coins49042.2020.9191672.
Texto completoMetwaly, Aly, Jorge Peña Queralta, Victor Kathan Sarker, Tuan Nguyen Gia, Omar Nasir y Tomi Westerlund. "Edge Computing with Embedded AI". En INTESA2019: INTelligent Embedded Systems Architectures and Applications Workshop 2019. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3372394.3372397.
Texto completoCosta, Bárbara, Octavian Postolache y John Araujo. "From cloud AI to embedded AI in cardiac healthcare". En 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2023. http://dx.doi.org/10.1109/i2mtc53148.2023.10176077.
Texto completoGhajargar, Maliheh, Jeffrey Bardzell, Alison Smith Renner, Peter Gall Krogh, Kristina Höök, David Cuartielles, Laurens Boer y Mikael Wiberg. "From ”Explainable AI” to ”Graspable AI”". En TEI '21: Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3430524.3442704.
Texto completoYoo, Hoi-Jun. "Mobile/embedded DNN and AI SoCs". En 2018 International Symposium on VLSI Design, Automation and Test (VLSI-DAT). IEEE, 2018. http://dx.doi.org/10.1109/vlsi-dat.2018.8373285.
Texto completoYoo, Hoi-Jun. "Mobile/embedded DNN and AI SoCs". En 2018 International Symposium on VLSI Technology, Systems and Application (VLSI-TSA). IEEE, 2018. http://dx.doi.org/10.1109/vlsi-tsa.2018.8403807.
Texto completoGhajargar, Maliheh, Jeffrey Bardzell, Alison Marie Smith-Renner, Kristina Höök y Peter Gall Krogh. "Graspable AI: Physical Forms as Explanation Modality for Explainable AI". En TEI '22: Sixteenth International Conference on Tangible, Embedded, and Embodied Interaction. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3490149.3503666.
Texto completoBlazevic, Romana, Omar Veledar y Georg Macher. "Insides to Trustworthy AI-Based Embedded Systems". En WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2014.
Texto completoKum, Seungwoo, Miseon Yu, Youngkee Kim, Jaewon Moon y Silvio Cretti. "AI Management Platform with Embedded Edge Cluster". En 2021 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2021. http://dx.doi.org/10.1109/icce50685.2021.9427731.
Texto completoBrandalero, Marcelo, Mitko Veleski, Hector Gerardo Munoz Hernandez, Muhammad Ali, Laurens Le Jeune, Toon Goedeme, Nele Mentens et al. "AITIA: Embedded AI Techniques for Industrial Applications". En 2021 31st International Conference on Field-Programmable Logic and Applications (FPL). IEEE, 2021. http://dx.doi.org/10.1109/fpl53798.2021.00071.
Texto completoInformes sobre el tema "Embedded AI"
Volz, Richard A. Report on the Embedded AI Languages Workshop Held in Ann Arbor, Michigan on 16-18 November 1988. Fort Belvoir, VA: Defense Technical Information Center, enero de 1990. http://dx.doi.org/10.21236/ada218531.
Texto completoDafflon, Baptiste, S. Wielandt, S. Uhlemann, Haruko Wainwright, K. Bennett, Jitendra Kumar, Sebastien Biraud, Susan Hubbard y Stan Wullschleger. Revolutionizing observations and predictability of Arctic system dynamics through next-generation dense, heterogeneous and intelligent wireless sensor networks with embedded AI. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1769774.
Texto completoBeiker, Sven. Next-generation Sensors for Automated Road Vehicles. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, febrero de 2023. http://dx.doi.org/10.4271/epr2023003.
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