FPGA based Power-Efficient Edge Server to Accelerate Speech Interface for Socially Assistive Robotics

Abstract

Socially Assistive Robotics (SAR) is a sustainable solution for the growing elderly and disabled population requiring proper care and supervision. Internet of Things (IoT) and Edge Computing can leverage SAR by providing in-house computation of connected devices and offering a secure, autonomous, and power-efficient framework. In this study, we have proposed using a System-on-Chip (SoC) based device as an edge server, which provides a local speech recognition interface for connected IoT devices in the targeted area. Convolutional Neural Network (CNN) is used to detect a set of frequently used speech commands which are useful to control home appliances and interact with assistive robots. Proposed CNN achieves state-of-the-art accuracy with a meager computing budget. It delivers 96.14% accuracy with a 20X smaller number of parameters and 137X fewer Floating Point Operations (FLOPS) compared to similarly performing CNN networks. To address the challenge of latency requirement for practical applications, parallelization of CNN helped to achieve 6.67X times faster inference speed than its base implementation. Lastly, implementing CNN on SoC-based edge device achieved at least 5X and 7X reduction in net power consumption compared to GPU and CPU devices respectively.