Code Generation of Graph-Based Vision Processing for Multiple CUDA Cores SoC Jetson TX

Tsur, Elishai Ezra; Madar, Elyassaf; Danan, Natan

doi:10.1109/mcsoc2018.2018.00013

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Having GPUs (or other ML-relevant acceleration hardware) on the edge is important since they allow for efficient neural network-driven inference and training [150]. For example, NVIDIA was behind the development of the Jetson microprocessors with an embedded GPU having varying complexities on which various robotic [151] and vision [152] edge applications can be deployed. Real-time learning with SNN requires its own dedicated (silicon neuron-based) hardware, such as Intel's Loihi or OZ [153].…”

Section: Ai On the Edgementioning

confidence: 99%

Intelligent Robotics in Pediatric Cooperative Neurorehabilitation: A Review

Ezra Tsur,

Elkana

2024

Robotics

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The landscape of neurorehabilitation is undergoing a profound transformation with the integration of artificial intelligence (AI)-driven robotics. This review addresses the pressing need for advancements in pediatric neurorehabilitation and underscores the pivotal role of AI-driven robotics in addressing existing gaps. By leveraging AI technologies, robotic systems can transcend the limitations of preprogrammed guidelines and adapt to individual patient needs, thereby fostering patient-centric care. This review explores recent strides in social and diagnostic robotics, physical therapy, assistive robotics, smart interfaces, and cognitive training within the context of pediatric neurorehabilitation. Furthermore, it examines the impact of emerging AI techniques, including artificial emotional intelligence, interactive reinforcement learning, and natural language processing, on enhancing cooperative neurorehabilitation outcomes. Importantly, the review underscores the imperative of responsible AI deployment and emphasizes the significance of unbiased, explainable, and interpretable models in fostering adaptability and effectiveness in pediatric neurorehabilitation settings. In conclusion, this review provides a comprehensive overview of the evolving landscape of AI-driven robotics in pediatric neurorehabilitation and offers valuable insights for clinicians, researchers, and policymakers.

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Section: Ai On the Edgementioning

confidence: 99%

Intelligent Robotics in Pediatric Cooperative Neurorehabilitation: A Review

Ezra Tsur,

Elkana

2024

Robotics

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Machine Learning at the Network Edge: A Survey

et al. 2021

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Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e., close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.

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Hardware Solutions for Low-Power Smart Edge Computing

Wisniewski¹,

Bec²,

Boguszewski³

et al. 2022

JLPEA

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The edge computing paradigm for Internet-of-Things brings computing closer to data sources, such as environmental sensors and cameras, using connected smart devices. Over the last few years, research in this area has been both interesting and timely. Typical services like analysis, decision, and control, can be realized by edge computing nodes executing full-fledged algorithms. Traditionally, low-power smart edge devices have been realized using resource-constrained systems executing machine learning (ML) algorithms for identifying objects or features, making decisions, etc. Initially, this paper discusses recent advances in embedded systems that are devoted to energy efficient ML algorithm execution. A survey of the mainstream embedded computing devices for low power IoT and edge computing is then presented. Finally, CYSmart is introduced as an innovative smart edge computing system. Two operational use cases are presented to illustrate its power efficiency.

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Code Generation of Graph-Based Vision Processing for Multiple CUDA Cores SoC Jetson TX

Cited by 4 publications

References 19 publications

Intelligent Robotics in Pediatric Cooperative Neurorehabilitation: A Review

Intelligent Robotics in Pediatric Cooperative Neurorehabilitation: A Review

Machine Learning at the Network Edge: A Survey

Hardware Solutions for Low-Power Smart Edge Computing

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