While Shield AI uses cloud-based data centers (often via AWS)
aws.amazon.com/blogs/publics… for training the AI Foundational Models (FMs), running large-scale simulations, and managing software updates across a fleet, the entire “mission-time” tracking happens entirely at the edge locally on the aircrafts on-board hardware.
The onboard hardware powering Hivemind’s edge-level tracking and mission autonomy is centered on high-performance modular NVIDIA GPU upgrades
shield.ai/shield-ai-unveils-… integrated directly into the aircraft's payload. These GPUs are specifically tasked with running the Hivemind AI pilot and its computer vision tracking algorithms.
shield.ai/autonomy-for-the-w… This hardware allows the AI to process massive amounts of sensor data locally, ensuring the aircraft can "perceive, think, and act" without a connection to a remote data center.
Shield AI has successfully integrated Hivemind onto the Parry Labs Edge Compute Micro (EC Micro), a rugged, small-form-factor device that combines high-density CPU and advanced GPU processing. The tracking algorithms run within the same EdgeOS middleware that manages the rest of the autonomy, ensuring the "AI Pilot" can react immediately to what the "Eyes" (the tracker) see.
shield.ai/edge-os/
shield.ai/hivemind-edgeos-a-…
At the heart of such autonomy lies a robust perception stack, which enables on-the-edge decision-making by seamlessly integrating sensors, algorithms, and data processing to gather, interpret, and respond to the environment in real time. This capability is foundational for intelligent autonomy and resilience, empowering autonomous platforms to operate safely and effectively in denied, degraded, intermittent, or low-bandwidth (DDIL) environments where GNSS and communications are unavailable.
shield.ai/the-critical-role-…
This hardware stack is what enables "Level 4" or higher autonomy, where the vehicle can complete its entire mission—including target tracking and swarming—even when GPS and communications are fully jammed.