AITIA: Embedded AI Techniques for Embedded Industrial Applications

Brandalero, Marcelo; Ali, Mohammad; Jeune, Laurens Le; Hernandez, Hector Gerardo Muñoz; Veleski, Mitko; Silva, Bruno da; Lemeire, Jan; Beeck, Kristof Van; Touhafi, Abdellah; Goedemé, Toon; Mentens, Nele; Göhringer, Diana; Hübner, Michael

doi:10.1109/coins49042.2020.9191672

Cited by 16 publications

(7 citation statements)

References 36 publications

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“…Concerning knowledge, two principal domains can be delineated. Firstly, the discourse addresses the scarcity of skilled employees (Basri 2020;Brezani et al 2022), and secondly, it encompasses the general understanding of AI, such as knowledge about the procedure for AI implementation and knowledge about existing AI application possibilities (Brandalero et al 2020;Welte et al 2020).…”

Section: Challenges Regarding Ai Implementation In Smesmentioning

confidence: 99%

Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges

Oldemeyer,

Jede,

Teuteberg

2024

Manag Rev Q

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While the topic of artificial intelligence (AI) in multinational enterprises has been receiving attention for some time, small and medium enterprises (SMEs) have recently begun to recognize the potential of this new technology. However, the focus of previous research and AI applications has therefore mostly been on large enterprises. This poses a particular issue, as the vastly different starting conditions of various company sizes, such as data availability, play a central role in the context of AI. For this reason, our systematic literature review, based on the PRISMA protocol, consolidates the state of the art of AI with an explicit focus on SMEs and highlights the perceived challenges regarding implementation in this company size. This allowed us to identify various business activities that have been scarcely considered. Simultaneously, it led to the discovery of a total of 27 different challenges perceived by SMEs in the adoption of AI. This enables SMEs to apply the identified challenges to their own AI projects in advance, preventing the oversight of any potential obstacles or risks. The lack of knowledge, costs, and inadequate infrastructure are perceived as the most common barriers to implementation, addressing social, economic, and technological aspects in particular. This illustrates the need for a wide range of support for SMEs regarding an AI introduction, which covers various subject areas, like funding and advice, and differentiates between company sizes.

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Section: Challenges Regarding Ai Implementation In Smesmentioning

confidence: 99%

Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges

Oldemeyer,

Jede,

Teuteberg

2024

Manag Rev Q

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“…Funding: This work was partially supported by the European Regional Development Fund (ERDF) and the Brussels-Capital Region-Innoviris within the framework of the Operational Programme 2014-2020 through the ERDF-2020 Project ICITYRDI.BRU. This work is also part of the COllective Research NETworking (CORNET) project "AITIA: Embedded AI Techniques for Industrial Applications" [59].…”

Section: Data Availability Statementmentioning

confidence: 99%

M3-AC: A Multi-Mode Multithread SoC FPGA Based Acoustic Camera

et al. 2021

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Acoustic cameras allow the visualization of sound sources using microphone arrays and beamforming techniques. The required computational power increases with the number of microphones in the array, the acoustic images resolution, and in particular, when targeting real-time. Such a constraint limits the use of acoustic cameras in many wireless sensor network applications (surveillance, industrial monitoring, etc.). In this paper, we propose a multi-mode System-on-Chip (SoC) Field-Programmable Gate Arrays (FPGA) architecture capable to satisfy the high computational demand while providing wireless communication for remote control and monitoring. This architecture produces real-time acoustic images of 240 × 180 resolution scalable to 640 × 480 by exploiting the multithreading capabilities of the hard-core processor. Furthermore, timing cost for different operational modes and for different resolutions are investigated to maintain a real time system under Wireless Sensor Networks constraints.

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“…To overcome such problems, embedded AI techniques are more and more diffusing to propose efficient and not computational expensive data driven analysis approach, directly executable on devices’ hardware of industrial equipment (Brandalero et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

A deep attention based approach for predictive maintenance applications in IoT scenarios

Luca

Ferraro²,

Galli³

et al. 2023

JMTM

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PurposeThe recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.Design/methodology/approachIn this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.FindingsThe achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.Research limitations/implicationsA comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.Practical implicationsThe proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.Originality/valueThe proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

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AITIA: Embedded AI Techniques for Embedded Industrial Applications

Cited by 16 publications

References 36 publications

Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges

Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges

M3-AC: A Multi-Mode Multithread SoC FPGA Based Acoustic Camera

A deep attention based approach for predictive maintenance applications in IoT scenarios

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