  We investigate fusing several unreliable computational units that perform the
same task. We model an unreliable computational outcome as an additive
perturbation to its error-free result in terms of its fidelity and cost. We
analyze performance of repetition-based strategies that distribute cost across
several unreliable units and fuse their outcomes. When the cost is a convex
function of fidelity, the optimal repetition-based strategy in terms of
incurred cost while achieving a target mean-square error (MSE) performance may
fuse several computational units. For concave and linear costs, a single more
reliable unit incurs lower cost compared to fusion of several lower cost and
less reliable units while achieving the same MSE performance. We show how our
results give insight into problems from theoretical neuroscience, circuits, and
crowdsourcing.
