  Keyword Spotting nowadays is an integral part of speech-oriented user
interaction targeted for smart devices. To this extent, neural networks are
extensively used for their flexibility and high accuracy. However, coming up
with a suitable configuration for both accuracy requirements and hardware
deployment is a challenge. We propose a regression-based network exploration
technique that considers the scaling of the network filters ($s$) and
quantization ($q$) of the network layers, leading to a friendly and
energy-efficient configuration for FPGA hardware implementation. We experiment
with different combinations of $\mathcal{NN}\scriptstyle\langle q,\,s\rangle
\displaystyle$ on the FPGA to profile the energy consumption of the deployed
network so that the user can choose the most energy-efficient network
configuration promptly. Our accelerator design is deployed on the Xilinx AC 701
platform and has at least 2.1$\times$ and 4$\times$ improvements on energy and
energy efficiency results, respectively, compared to recent hardware
implementations for keyword spotting.
