On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems

Conceitos essenciais

Fully on-device domain adaptation system achieves significant accuracy gains in noisy environments for keyword spotting models.

Resumo

Keyword spotting accuracy can degrade in noisy environments, necessitating on-site adaptation. This work proposes a fully on-device domain adaptation system that achieves up to 14% accuracy gains over robust keyword spotting models. The system enables on-device learning with minimal memory and labeled utterances, showcasing the ability to recover accuracy after adapting to complex speech noise. Domain adaptation is demonstrated on ultra-low-power microcontrollers with efficient energy consumption. The study addresses noise-robustness, low-power microcontrollers, extreme edge computing, TinyML, and keyword spotting.

Estatísticas

We enable on-device learning with less than 10 kB of memory.
Achieving up to 14% accuracy gains over already-robust keyword spotting models.
Demonstrated domain adaptation can be achieved on ultra-low-power microcontrollers with as little as 806 mJ in only 14 s.

Citações

"We propose a fully on-device domain adaptation system achieving up to 14% accuracy gains over already-robust keyword spotting models."
"We demonstrate that domain adaptation can be achieved on ultra-low-power microcontrollers with as little as 806 mJ in only 14 s."

Principais Insights Extraídos De

On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems

by Cristian Cio... às arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10549.pdf

Perguntas Mais Profundas

How does the proposed ODDA methodology compare to traditional offline training methods

The proposed On-Device Domain Adaptation (ODDA) methodology differs from traditional offline training methods in several key aspects. Firstly, ODDA enables adaptation to on-site noise conditions entirely on the edge device, ensuring that the model can specialize and recover accuracy losses caused by unseen noises. In contrast, traditional offline training relies on pre-collected data sets and may not be robust enough to handle real-world variations in noise levels.
Secondly, ODDA allows for post-deployment adaptation at the extreme edge with minimal memory and storage requirements. This is crucial for privacy-by-design systems where sensitive data does not need to be transmitted off-device for retraining purposes. In comparison, traditional offline training often involves large datasets stored remotely which may raise privacy concerns and incur communication energy costs.
Furthermore, ODDA focuses on efficient domain adaptation using limited resources such as memory and computational power available on low-power embedded systems like microcontrollers. By optimizing the learning process within these constraints, ODDA showcases a practical approach towards achieving accurate keyword spotting models in noisy environments without compromising performance or energy efficiency.

What are the implications of resource constraints on implementing ODDA in real-world applications

Resource constraints play a significant role in implementing On-Device Domain Adaptation (ODDA) methodologies in real-world applications. These constraints impact various aspects of the deployment process:

Memory Limitations: Limited read-write memory poses challenges when updating model parameters during domain adaptation. The size of trainable parameters, activations, gradients needed for backpropagation must fit within the available memory space.

Storage Constraints: Off-chip storage limitations affect storing frozen model parameters and prerecorded utterances used during training sessions for domain adaptation.

Energy Consumption: Energy-efficient processing is essential for battery-operated devices running always-on applications like keyword spotting systems.

Addressing these resource constraints requires innovative strategies such as partial freezing of trainable parameters or adapting only specific layers of the model to minimize memory usage while maintaining accuracy gains through domain adaptation.

How might the findings of this study impact the development of future TinyML systems

The findings of this study have significant implications for future TinyML system development:

Efficient On-Device Learning: The demonstrated success of On-Device Domain Adaptation (ODDA) highlights its potential to enable efficient post-deployment adaptations at the extreme edge without relying on external servers or cloud computing resources.

Privacy Preservation: By conducting all learning processes locally on low-power embedded devices like microcontrollers, ODDA ensures user data privacy by design while minimizing communication overhead associated with transferring sensitive information off-device.

Optimized Resource Utilization: The resource-constrained ODDA approach showcased in this study provides insights into how developers can optimize memory usage and reduce energy consumption during domain adaptation tasks on tiny ML platforms.

4Enhanced Noise Robustness: Implementing adaptive techniques like ODDA can significantly improve noise robustness in keyword spotting models deployed in diverse environments with varying levels of background noise intensity.