Error resilient and energy efficient MRF message-passing-based stereo matching

Message-passing-based inference algorithms have immense importance in real-world applications. In this paper, error resiliency of a message passing based Markov random field (MRF) stereo matching hardware is explored and enhanced through the application of statistical error compensation. Error resiliency is of particular interest for subnanometer and postsilicon devices. The inherent robustness of iteration-based MRF inference algorithms is explored and shows that small errors are tolerable, while large errors degrade the performance significantly. Based on these error characteristics, algorithmic noise tolerance (ANT) has been applied at the arithmetic, iteration, and system levels. Introducing timing errors via voltage overscaling, at the arithmetic level, results show that the ANT-based hardware can tolerate an error rate of 21.3%, with performance degradation of only 3.5% at an overhead of 97.4%, compared with an error-free hardware with an energy savings of 39.7%. To reduce compensation complexity, iteration and system-level compensation was explored. Results show that, compared with arithmetic level, system-level compensation reduces overhead to 59%, while maintaining stereo matching performance with only 2.5% degradation with 16% additional power savings. These results are verified via FPGA emulation with timing errors induced within the message passing unit via relaxed synthesis.