In addition to the C7 NPU compute cluster, the TDA4VH-Q1 also includes on-chip vision processing accelerators (VPAC) and depth and motion perception accelerators (DMPAC). These accelerators handle image and motion pre-processing and are designed to work in conjunction with the deep learning algorithms for computer vision applications. The VPAC pipeline performs hardware-accelerated lens distortion correction, noise filtering, and image statistics extraction, enabling camera frames to be conditioned for AI inference without consuming C7 NPU cycles. This is particularly valuable in multi-camera drone configurations where several independent video streams must be processed simultaneously.

The DMPAC computes dense stereo depth maps and per-pixel optical flow vectors in hardware, offloading two of the most computationally demanding operations in autonomous drone navigation. Stereo depth maps provide the obstacle proximity data required for real-time collision avoidance. Dense optical flow provides a pixel-level motion estimate that can be fused with inertial measurement unit (IMU) data to generate velocity estimates for stable hover in GPS-denied environments - a capability critical for indoor inspection, tunnel survey, and contested-spectrum operations. Both outputs are produced with deterministic latency, making them practical for closed-loop feedback into the flight controller via Micro Air Vehicle Link (MAVLink) [13] or Robot Operating System 2 (ROS 2) [16].