Invertible Light Technology (ILT) is a physics- and geometry-based depth-sensing approach designed for low algorithmic overhead. Unlike compute-heavy or correlation-intensive methods, ILT performs depth reconstruction using deterministic, invertible math, enabling depth estimation with minimal compute, small memory footprint, and predictable latency. Because the algorithm is lightweight, ILT can run alongside host applications on low-cost MCUs or SoCs, eliminating the need for dedicated depth processors, GPUs, or NPUs for depth calculation. This reduces system power, cost, and thermal load while preserving real-time behavior. By minimizing depth-processing overhead, ILT frees processors and accelerators for higher-level AI tasks on depth data—such as object detection, tracking, scene understanding, and sensor fusion—enabling cleaner system partitioning and more scalable embedded vision architectures. In this session we introduce ILT and show performance under real-world embedded constraints: compute budget, memory, power, determinism, robustness, scalability and BOM cost.