  Motion planning is a computationally intensive and well-studied problem in
autonomous robots. However, motion planning hardware accelerators (MPA) must be
soft-error resilient for deployment in safety-critical applications, and
blanket application of traditional mitigation techniques is ill-suited due to
cost, power, and performance overheads. We propose Collision Exposure Factor
(CEF), a novel metric to assess the failure vulnerability of circuits
processing spatial relationships, including motion planning. CEF is based on
the insight that the safety violation probability increases with the surface
area of the physical space exposed by a bit-flip. We evaluate CEF on four MPAs.
We demonstrate empirically that CEF is correlated with safety violation
probability, and that CEF-aware selective error mitigation provides 12.3x,
9.6x, and 4.2x lower Failures-In-Time (FIT) rate on average for the same amount
of protected memory compared to uniform, bit-position, and
access-frequency-aware selection of critical data. Furthermore, we show how to
employ CEF to enable fault characterization using 23,000x fewer fault injection
(FI) experiments than exhaustive FI, and evaluate our FI approach on different
robots and MPAs. We demonstrate that CEF-aware FI can provide insights on
vulnerable bits in an MPA while taking the same amount of time as uniform
statistical FI. Finally, we use the CEF to formulate guidelines for designing
soft-error resilient MPAs.
