A Review of Artificial Intelligence in Embedded Systems

Zhang, Zhaoyun; Li, Jingpeng

doi:10.3390/mi14050897

Cited by 15 publications

(4 citation statements)

References 60 publications

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“…Today, enabling "intelligence" in technology has become a key strategy of research spanning different disciplines. [130,131] Neuromorphic technology shows great potential to dissolve the issue of display. In the whole process of display (from image acquisition to luminescence unit), neuromorphic devices play different roles, such as bioinspired sensing, neural driving, and smart luminescence.…”

Section: Challenges and Perspectivementioning

confidence: 99%

Toward Intelligent Display with Neuromorphic Technology

Zhang,

Liu,

Liu

et al. 2024

Advanced Materials

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In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full‐scene display and realistic display. Incorporating ‘intelligence’ into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light‐emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all‐in‐one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross‐scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full‐scene display and realistic display with high efficiency and low power consumption. In this review, we offer a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. We delve into its state‐of‐the‐art designs and potential future developments aimed at revolutionizing display technology.This article is protected by copyright. All rights reserved

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Section: Challenges and Perspectivementioning

confidence: 99%

Toward Intelligent Display with Neuromorphic Technology

Zhang,

Liu,

Liu

et al. 2024

Advanced Materials

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“…As defined in Mwase et al [10], the type of devices utilized in the edge layer can be very diverse ranging from resourceful hardware with higher latency to very limited hardware battery-powered devices that enable ultra-low latency. Consequently, the application of machine learning techniques in devices with reduced hardware like system on chip (SoC) is denominated embedded AI [3] and when the hardware is still more constrained like in micro-controller units (MCUs) is Tiny Machine Learning (TinyML) [11]. The relationship between these paradigms is illustrated in Figure 1.…”

Section: Edge Paradigmsmentioning

confidence: 99%

“…In this way, agility and decision-making are boosted in this computing layer. This area of the EC is referred to as Embedded AI [3]. Consequently, edge devices with artificial intelligence accelerators integrated are emerging such as Graphical Processing Units (GPUs) and Tensor Processing Units (TPU).…”

Section: Introductionmentioning

confidence: 99%

Analysing Edge Computing Devices for the Deployment of Embedded AI

Garcia-Perez,

Miñón,

Torre-Bastida

et al. 2023

Sensors

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In recent years, more and more devices are connected to the network, generating an overwhelming amount of data. This term that is booming today is known as the Internet of Things. In order to deal with these data close to the source, the term Edge Computing arises. The main objective is to address the limitations of cloud processing and satisfy the growing demand for applications and services that require low latency, greater efficiency and real-time response capabilities. Furthermore, it is essential to underscore the intrinsic connection between artificial intelligence and edge computing within the context of our study. This integral relationship not only addresses the challenges posed by data proliferation but also propels a transformative wave of innovation, shaping a new era of data processing capabilities at the network’s edge. Edge devices can perform real-time data analysis and make autonomous decisions without relying on constant connectivity to the cloud. This article aims at analysing and comparing Edge Computing devices when artificial intelligence algorithms are deployed on them. To this end, a detailed experiment involving various edge devices, models and metrics is conducted. In addition, we will observe how artificial intelligence accelerators such as Tensor Processing Unit behave. This analysis seeks to respond to the choice of a device that best suits the necessary AI requirements. As a summary, in general terms, the Jetson Nano provides the best performance when only CPU is used. Nevertheless the utilisation of a TPU drastically enhances the results.

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“…Different types of such networks have been shown to be universal approximators of some classes of functions (e.g., [13][14][15]). Artificial neural networks are increasingly used in embedded artificial intelligence (AI) systems, i.e., systems that run on computational devices with finite amounts of computer memory (e.g., [16]). We will refer to embedded AI as finite AI to emphasize the fact that finite AI systems are realized on computational devices with finite amounts of computational memory.…”

Section: Introductionmentioning

confidence: 99%