👉 Buck computing is an innovative approach to machine learning and AI developed by Google, aimed at making AI more accessible and efficient. It focuses on creating lightweight, specialized hardware designed to perform inference tasks—such as running machine learning models—much faster and with significantly lower power consumption compared to traditional GPUs. This is achieved through a novel architecture that includes a compact neural processing unit (NPU) and a sparse compute unit (SCU), which work together to process only the most relevant parts of an input, drastically reducing computational overhead. By offloading inference tasks to these specialized chips, Buck computing enables real-time processing on edge devices like smartphones and IoT gadgets, making AI capabilities more practical for everyday applications without the need for constant cloud connectivity. This approach not only enhances performance but also addresses privacy concerns by keeping data processing local, thus aligning with the growing demand for on-device AI solutions.
