  Probabilistic generative neural networks are useful for many applications,
such as image classification, speech recognition and occlusion removal.
However, the power budget for hardware implementations of neural networks can
be extremely tight. To address this challenge we describe a design methodology
for using approximate computing methods to implement Approximate Deep Belief
Networks (ApproxDBNs) by systematically exploring the use of (1) limited
precision of variables; (2) criticality analysis to identify the nodes in the
network which can operate with such limited precision while allowing the
network to maintain target accuracy levels; and (3) a greedy search methodology
with incremental retraining to determine the optimal reduction in precision to
enable maximize power savings under user-specified accuracy constraints.
Experimental results show that significant bit-length reduction can be achieved
by our ApproxDBN with constrained accuracy loss.
