Sponsored by Texas Instruments: Advancing artificial intelligence to the next level involves the implementation of robust deep-learning neural networks. Highly integrated SoCs help simplify the ...

However, software development and delivery are a major pain point for automotive system developers, especially for deep-learning designs that have been predominantly built for consumer and server ...

Abstract: New generation of embedded systems with superior intelligence, energy efficiency, and performance have emerged as a result of the merging of deep learning with Very-Large-Scale Integration ...

Abstract: Deep learning algorithms are used in various advanced applications, including computer vision, large language models and many others due to their increasing success over traditional ...

Potential customers are found in all areas using deep ... embedded systems. The software can be used in all application areas where deep learning is used, from self-driving vehicles to the Internet of ...

Additionally, deploying deep learning algorithms on embedded platforms often involves leveraging hardware acceleration techniques such as GPU acceleration, specialized neural network accelerators, or ...

In this work, we explore the possibility of employing deep learning in graph clustering. We propose a simple method, which first learns a nonlinear embedding of the original graph by stacked ...

including algorithm design, graph theory, and machine learning. In January 2020, he received his joint Ph.D. from the University of Calabria and Université Claude Bernard Lyon 1 (Lyon, France), with a ...