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

A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting