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Optimizing Deep Learning for Real-World Deployment: The Role of Hardware-Aware Neural Architecture Search

Optimizing Deep Learning for Real-World Deployment: The Role of Hardware-Aware Neural Architecture Search

Speaker: Kaoutar El Maghraoui – Yorktown Heights, NY, United States
Topic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing , Hardware, Power and Energy

Abstract

Hardware-aware Neural Architecture Search (NAS) has emerged as a crucial approach to optimize the design of deep neural networks (DNNs) for specific hardware platforms, unlocking unprecedented performance and efficiency gains. As the demand for AI applications continues to surge across various domains, from edge devices to data centers, the need for tailored neural architectures optimized for diverse hardware configurations becomes paramount.

We present the landscape of hardware-aware NAS, exploring cutting-edge methodologies and advancements in designing neural architectures with hardware constraints in mind. Researchers can navigate the complex trade-offs between model accuracy, computational efficiency, and resource constraints by incorporating hardware awareness into the NAS process.

This lecture covers the principles, algorithms, and techniques behind hardware-aware NAS through a comprehensive overview of recent research and practical applications. From leveraging hardware-specific performance metrics to designing robust hardware estimators to strategies, participants will gain insights into the techniques driving the development of efficient and effective deep-learning models tailored for real-world deployment scenarios. By embracing hardware awareness in NAS, practitioners and researchers can pave the way for the next generation of AI systems that are accurate, energy efficient, and optimized for the underlying hardware architectures, paving the way to a much-needed era of scalable and sustainable AI solutions.

About this Lecture

Number of Slides: 60
Duration: 60 minutes
Languages Available: English
Last Updated:

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