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.
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