# An Optimized Recurrent Unit for Ultra-Low-Power Keyword Spotting

## TL;DR

This paper introduces eGRU, an optimized recurrent neural network unit designed for ultra-low-power devices, enabling efficient keyword spotting and acoustic event detection on micro-controllers with minimal accuracy loss.

## Contribution

The paper presents the eGRU architecture, a highly efficient variant of GRU tailored for resource-constrained edge devices, with significant improvements in speed and size while maintaining accuracy.

## Key findings

- eGRU is 60x faster than standard GRU
- eGRU is 10x smaller than standard GRU
- Achieves 95.3% accuracy on embedded hardware

## Abstract

There is growing interest in being able to run neural networks on sensors, wearables and internet-of-things (IoT) devices. However, the computational demands of neural networks make them difficult to deploy on resource-constrained edge devices. To meet this need, our work introduces a new recurrent unit architecture that is specifically adapted for on-device low power acoustic event detection (AED). The proposed architecture is based on the gated recurrent unit (`GRU') but features optimizations that make it implementable on ultra-low power micro-controllers such as the Arm Cortex M0+. Our new architecture, the Embedded Gated Recurrent Unit (eGRU) is demonstrated to be highly efficient and suitable for short-duration AED and keyword spotting tasks. A single eGRU cell is 60x faster and 10x smaller than a GRU cell. Despite its optimizations, eGRU compares well with GRU across tasks of varying complexities. The practicality of eGRU is investigated in a wearable acoustic event detection application. An eGRU model is implemented and tested on the Arm Cortex M0-based Atmel ATSAMD21E18 processor. The Arm M0+ implementation of the eGRU model compares favorably with a full precision GRU that is running on a workstation. The embedded eGRU model achieves a classification accuracy 95.3%, which is only 2% less than the full precision GRU.