  With computers getting more and more powerful and integrated in our daily
lives, the focus is increasingly shifting towards more human-friendly
interfaces, making Automatic Speech Recognition (ASR) a central player as the
ideal means of interaction with machines. Consequently, interest in speech
technology has grown in the last few years, with more systems being proposed
and higher accuracy levels being achieved, even surpassing \textit{Human
Accuracy}. While ASR systems become increasingly powerful, the computational
complexity also increases, and the hardware support have to keep pace. In this
paper, we propose a technique to improve the energy-efficiency and performance
of ASR systems, focusing on low-power hardware for edge devices. We focus on
optimizing the DNN-based Acoustic Model evaluation, as we have observed it to
be the main bottleneck in state-of-the-art ASR systems, by leveraging run-time
information from the Beam Search. By doing so, we reduce energy and execution
time of the acoustic model evaluation by 25.6% and 25.9%, respectively, with
negligible accuracy loss.
