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Vision-based perception has become fundamental to everything from automotive safety systems and autonomous robots to heavy-duty drones. In each of these systems, the central challenge is the same: Raw image data from multiple cameras must be ingested, processed, and fed to AI inference pipelines with minimal latency to detect objects or other details in the surrounding area and react to them. The further challenge is staying within strict power and cost constraints.

Typically, image processing is performed within the system's main system-on-chip (SoC). But as camera counts grow and AI workloads intensify, that architecture is reaching its limits. One solution is to select a more powerful SoC, but this is expensive and it doesn’t resolve the fundamental issue of DRAM bandwidth shared across all compute blocks.