Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge

2025-02-15

AI Summary

Existing systems for ultra-edge audio sensing—e.g., gunshot detection using miniature microphone arrays—suffer from three critical bottlenecks: high energy consumption, high inference latency, and absence of online learning capability. To address these, this paper proposes a near-sensor hyperdimensional intelligent sensing architecture. It introduces the first synergistic design integrating FFT-based feature extraction, a lightweight CNN, and hyperdimensional computing (HDC), co-optimized for ASIC hardware mapping and Edge TPU acceleration. The architecture enables millisecond-scale inference, continual online learning, and ultra-low-power operation. Experimental results demonstrate that the software implementation reduces energy consumption by 82.1% over baseline methods with only a 1.39% accuracy degradation. The ASIC implementation achieves significantly higher energy efficiency than embedded CPUs and GPUs, fulfilling the stringent real-time requirements of extreme-edge audio sensing applications.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Humans and AI: Brain-Sensing and Analysis
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The escalating challenges of managing vast sensor-generated data, particularly in audio applications, necessitate innovative solutions. Current systems face significant computational and storage demands, especially in real-time applications like gunshot detection systems (GSDS), and the proliferation of edge sensors exacerbates these issues. This paper proposes a groundbreaking approach with a near-sensor model tailored for intelligent audio-sensing frameworks. Utilizing a Fast Fourier Transform (FFT) module, convolutional neural network (CNN) layers, and HyperDimensional Computing (HDC), our model excels in low-energy, rapid inference, and online learning. It is highly adaptable for efficient ASIC design implementation, offering superior energy efficiency compared to conventional embedded CPUs or GPUs, and is compatible with the trend of shrinking microphone sensor sizes. Comprehensive evaluations at both software and hardware levels underscore the model's efficacy. Software assessments through detailed ROC curve analysis revealed a delicate balance between energy conservation and quality loss, achieving up to 82.1% energy savings with only 1.39% quality loss. Hardware evaluations highlight the model's commendable energy efficiency when implemented via ASIC design, especially with the Google Edge TPU, showcasing its superiority over prevalent embedded CPUs and GPUs.

Problem

• Efficient real-time audio processing on edge devices
• Reducing computational and storage demands in audio applications
• Achieving energy efficiency with minimal quality loss

Innovation

• FFT and CNN for audio processing
• HyperDimensional Computing for efficiency
• ASIC design for energy savings