  Approximate computing methods have shown great potential for deep learning.
Due to the reduced hardware costs, these methods are especially suitable for
inference tasks on battery-operated devices that are constrained by their power
budget. However, approximate computing hasn't reached its full potential due to
the lack of work on training methods. In this work, we discuss training methods
for approximate hardware. We demonstrate how training needs to be specialized
for approximate hardware, and propose methods to speed up the training process
by up to 18X.
