Tag : AI hardware

A circular economy for AI hardware aims to reduce waste and maximize resource use by designing durable, repairable, and renewable devices, promoting reuse and recycling, and recovering valuable materials.

AI model optimization makes AI models faster, smaller, and more efficient by improving performance and reducing resource requirements. Key techniques include pruning, quantization, knowledge distillation, and hyperparameter tuning. This enables deployment on resource-constrained devices and reduces costs.

The huge energy consumption of AI comes from the intense computing power required to train and run complex models, especially in data centers. This results in a large carbon footprint and resource depletion, requiring urgent solutions such as efficient hardware, model optimization, and the use of renewable energy.