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How NPU works The working principle of NPU is based on the operation of neural networks. It uses dedicated computing units to handle matrix operations, which are the core operations in deep learning.
NPUs are able to process multiple data streams simultaneously, which makes them much faster than traditional processors at executing complex algorithms.
In addition, NPU can effectively reduce power consumption, which is especially important for mobile devices and edge computing scenarios.
The application scope of NPU is very wide. The following are several major application scenarios:
Autonomous vehicles: NPU can quickly process data from vehicle sensors and identify the surrounding environment in real time, thereby improving the safety and efficiency of autonomous driving.
Smartphones: Many modern smartphones are equipped with an NPU, which can accelerate the performance of image processing, voice recognition, and augmented reality (AR) applications.
Internet of Things (IoT) devices: In edge computing, NPUs can quickly process data locally, reducing reliance on the cloud, thereby improving response speed and data privacy.
Medical image analysis: NPU can accelerate the analysis of medical images, helping doctors make diagnoses faster and improving patient treatment outcomes.
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