  In recent years, we have witnessed a dramatic shift towards techniques driven
by neural networks for a variety of NLP tasks. Undoubtedly, neural language
models (NLMs) have reduced perplexity by impressive amounts. This progress,
however, comes at a substantial cost in performance, in terms of inference
latency and energy consumption, which is particularly of concern in deployments
on mobile devices. This paper, which examines the quality-performance tradeoff
of various language modeling techniques, represents to our knowledge the first
to make this observation. We compare state-of-the-art NLMs with "classic"
Kneser-Ney (KN) LMs in terms of energy usage, latency, perplexity, and
prediction accuracy using two standard benchmarks. On a Raspberry Pi, we find
that orders of increase in latency and energy usage correspond to less change
in perplexity, while the difference is much less pronounced on a desktop.
