  Spoken Language Understanding (SLU) systems consist of several machine
learning components operating together (e.g. intent classification, named
entity recognition and resolution). Deep learning models have obtained state of
the art results on several of these tasks, largely attributed to their better
modeling capacity. However, an increase in modeling capacity comes with added
costs of higher latency and energy usage, particularly when operating on low
complexity devices. To address the latency and computational complexity issues,
we explore a BranchyNet scheme on an intent classification scheme within SLU
systems. The BranchyNet scheme when applied to a high complexity model, adds
exit points at various stages in the model allowing early decision making for a
set of queries to the SLU model. We conduct experiments on the Facebook
Semantic Parsing dataset with two candidate model architectures for intent
classification. Our experiments show that the BranchyNet scheme provides gains
in terms of computational complexity without compromising model accuracy. We
also conduct analytical studies regarding the improvements in the computational
cost, distribution of utterances that egress from various exit points and the
impact of adding more complexity to models with the BranchyNet scheme.
