  The demand for efficient edge vision has spurred the interest in developing
stochastic computing approaches for performing image processing tasks.
Memristors with inherent stochasticity readily introduce probability into the
computations and thus enable stochastic image processing computations. Here, we
present a stochastic computing approach for edge detection, a fundamental image
processing technique, facilitated with memristor-enabled stochastic logics.
Specifically, we integrate the memristors with logic circuits and harness the
stochasticity from the memristors to realize compact stochastic logics for
stochastic number encoding and processing. The stochastic numbers, exhibiting
well-regulated probabilities and correlations, can be processed to perform
logic operations with statistical probabilities. This can facilitate
lightweight stochastic edge detection for edge visual scenarios characterized
with high-level noise errors. As a practical demonstration, we implement a
hardware stochastic Roberts cross operator using the stochastic logics, and
prove its exceptional edge detection performance, remarkably, with 95% less
computational cost while withstanding 50% bit-flip errors. The results
underscore the great potential of our stochastic edge detection approach in
developing lightweight, error-tolerant edge vision hardware and systems for
autonomous driving, virtual/augmented reality, medical imaging diagnosis,
industrial automation, and beyond.
