Transient and permanent errors in memory and
CPUs occur with alarming frequency. Although most of these
errors are masked at the hardware level or result in crashes, a
non-negligible number of them leads to Silent Data Corruptions
(SDCs), i.e., incorrect results of computations. Safety-critical
programs require a very high level of confidence that such faults
are detected and not propagated to the outside. Unfortunately,
state-of-the-art fault detection techniques generally assume a
limited Single Event Upset fault model, concentrating only on
transient faults.
We present ∆-encoding: a software-only approach to detect
hardware faults with very high probability. ∆-encoding makes no
assumptions on the rate and type of faults. Our approach com-
bines AN codes and duplicated instructions to harden programs
against transient and permanent hardware errors. Our evaluation
shows that ∆-encoding detects 99.997% of all injected errors with
performance slowdown of 2–4 times.