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Wearable AI devices, such as AI glasses, are gaining traction for their ability to provide voice-based assistance and enhance user experiences. However, these devices raise significant privacy concerns, as they may expose personal data to external companies when activated. The current reliance on cloud processing also leads to high costs, latency issues, and energy consumption challenges.
A proposed solution involves separating the encoder and decoder components of AI models, allowing for on-device processing that enhances privacy and efficiency. By utilizing lightweight encoders on wearables and offloading more intensive processing to smartphones, this architecture can significantly reduce latency and power consumption. This innovative approach not only protects user data but also extends battery life and improves overall device performance.
• Wearable AI devices face privacy and performance challenges.
• Encoder-decoder separation enhances efficiency and privacy in AI processing.
This technique allows for processing input signals on-device while offloading complex tasks to smartphones.
This approach integrates various data types, such as audio and visual inputs, for enhanced AI understanding.
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