In a world where everything from your fridge to fitness trackers competes for bandwidth, edge computing keeps operations smooth by processing data locally, enhancing speed and privacy.
Small Language Models (SLMs) are vital for efficient AI inference at the edge, enabling real-time learning while minimizing computational load on devices with limited resources.
Techniques like quantization and pruning can drastically reduce the size and speed of language models, making them appropriate for deployment on resource-constrained edge devices.
Google Edge TPU serves as an example of high-efficiency AI inference in edge computing, utilizing techniques of pruning and sparsity to manage resources effectively.
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