  Neural Architecture Search has proven an effective method of automating
architecture engineering. Recent work in the field has been to look for
architectures subject to multiple objectives such as accuracy and latency to
efficiently deploy them on different target hardware. Once-for-All (OFA) is one
such method that decouples training and search and is able to find
high-performance networks for different latency constraints. However, the
search phase is inefficient at incorporating multiple latency targets. In this
paper, we introduce two strategies (Top-down and Bottom-up) that use warm
starting and randomized network pruning for the efficient incorporation of
multiple latency targets in the OFA network. We evaluate these strategies
against the current OFA implementation and demonstrate that our strategies
offer significant running time performance gains while not sacrificing the
accuracy of the subnetworks that were found for each latency target. We further
demonstrate that these performance gains are generalized to every design space
used by the OFA network.
