Introduction
The traditional AI paradigm relies heavily on the cloud. However, latency, bandwidth costs, and privacy concerns are driving a fundamental shift: from a "centralized cloud brain" to "distributed edge intelligence."
Our Vision: Relay Inference Between Devices
Imagine two AI devices, both capable of local inference, vision, actuation, and control. Device A (e.g., an AI camera) detects and analyzes a target. Instead of streaming video, it generates a structured "Task" – containing action suggestions, coordinates, and confidence scores – and passes it to Device B (e.g., a robotic arm). Device B then takes over and executes the next steps without needing to re-analyze the raw data. This is "task handover."
Why This Matters: High-Value Applications
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Smart Factory: An AI quality inspection camera detects a minor defect, generates a repair path, and sends it directly to a robotic arm controller for immediate, localized action. No central server is needed.
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Autonomous Farming: A surveying drone identifies a pest outbreak and creates a "prescription map" (with coordinates and dosage) for a precise spraying robot.
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Smart Hospital Ward: A bedside monitor detects a patient trying to get up, assesses a fall risk, and alerts a mobile nursing robot to provide assistance.
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Smart Home: A smart doorbell identifies a courier and signals the robot vacuum to avoid that area until the delivery is complete.
Key Challenges
To realize this vision, we must solve:
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On-Device Compute: Each device must have sufficient local AI inference capability.
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Communication Decoupling: Devices should pass "semantic tasks" (intent), not raw data streams.
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Strategic Chip Selection: Choosing a chip partner while maintaining long-term flexibility and replaceability.