تقرير
A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting
| العنوان: | A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting |
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| المؤلفون: | Mazumder, Arnab Neelim, Mohsenin, Tinoosh |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | 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. Comment: accepted in tinyML Research Symposium 2022 |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2202.02361Test |
| رقم الانضمام: | edsarx.2202.02361 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |