Hardware Aware

Hardware-aware research focuses on designing and optimizing machine learning models and algorithms for specific hardware platforms, aiming to maximize efficiency (speed, energy consumption, memory usage) without sacrificing accuracy. Current efforts concentrate on large language models (LLMs), graph neural networks (GNNs), and deep neural networks (DNNs), employing techniques like neural architecture search (NAS) and hardware performance prediction to achieve optimal mappings between model architectures and hardware capabilities. This field is crucial for deploying computationally intensive AI applications on resource-constrained devices like edge computers and mobile phones, enabling broader accessibility and reducing the environmental impact of AI.