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For many applications in low-power, real-time robotics, stereo cameras are the sensors of choice for depth perception. Their biggest drawback, however, is that they do not directly sense depth maps; instead, these must be estimated through data-intensive processes. Motivated by applications in space and mobile robotics, we implement and evaluate an FPGA-accelerated adaptation of the ELAS algorithm. Despite offering one of the best tradeoffs between efficiency and accuracy, ELAS has only been shown to run at 1.5 − 3 fps on a high-end CPU. Our system preserves all intriguing properties of the original algorithm, such as the slanted plane priors, but can achieve a frame rate of 47fps whilst consuming under 4W of power. Unlike previous FPGA based designs, we take advantage of both components on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate more complex and computationally diverse algorithms for such low power, real-time systems.