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

arXiv: https://arxiv.org/abs/1902.05026 · HTML: https://mlsanity.com/paper/1902.05026/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks](https://mlsanity.com/paper/2201.03386/) (2022)
- [Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU](https://mlsanity.com/paper/2506.08911/) (2025)
- [Analog Gated Recurrent Neural Network for Detecting Chewing Events](https://mlsanity.com/paper/2208.01201/) (2022)
- [GhostRNN: Reducing State Redundancy in RNN with Cheap Operations](https://mlsanity.com/paper/2411.14489/) (2024)

## Abstract (arXiv)

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.