  The use of Deep Learning hardware algorithms for embedded applications is
characterized by challenges such as constraints on device power consumption,
availability of labeled data, and limited internet bandwidth for frequent
training on cloud servers. To enable low power implementations, we consider
efficient bitwidth reduction and pruning for the class of Deep Learning
algorithms known as Discriminative Deep Belief Networks (DDBNs) for
embedded-device classification tasks. We train DDBNs with both generative and
discriminative objectives under an approximate computing framework and analyze
their power-at-performance for supervised and semi-supervised applications. We
also investigate the out-of-distribution performance of DDBNs when the
inference data has the same class structure yet is statistically different from
the training data owing to dynamic real-time operating environments. Based on
our analysis, we provide novel insights and recommendations for choice of
training objectives, bitwidth values, and accuracy sensitivity with respect to
the amount of labeled data for implementing DDBN inference with minimum power
consumption on embedded hardware platforms subject to accuracy tolerances.
