# Wireless Hearables With Programmable Speech AI Accelerators

arXiv: https://arxiv.org/abs/2503.18698 · HTML: https://mlsanity.com/paper/2503.18698/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2025.

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

- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [TF-MLPNet: Tiny Real-Time Neural Speech Separation](https://mlsanity.com/paper/2508.03047/) (2025)
- [Micro Language Models Enable Instant Responses](https://mlsanity.com/paper/2604.19642/) (2026)
- [Transformer-based Neural Beamforming for Real-Time Speech Enhancement on Smart Low-Power Hearable Devices](https://mlsanity.com/paper/2609.33755/) (2026)

## Abstract (arXiv)

The conventional wisdom has been that designing ultra-compact, battery-constrained wireless hearables with on-device speech AI models is challenging due to the high computational demands of streaming deep learning models. Speech AI models require continuous, real-time audio processing, imposing strict computational and I/O constraints. We present NeuralAids, a fully on-device speech AI system for wireless hearables, enabling real-time speech enhancement and denoising on compact, battery-constrained devices. Our system bridges the gap between state-of-the-art deep learning for speech enhancement and low-power AI hardware by making three key technical contributions: 1) a wireless hearable platform integrating a speech AI accelerator for efficient on-device streaming inference, 2) an optimized dual-path neural network designed for low-latency, high-quality speech enhancement, and 3) a hardware-software co-design that uses mixed-precision quantization and quantization-aware training to achieve real-time performance under strict power constraints. Our system processes 6 ms audio chunks in real-time, achieving an inference time of 5.54 ms while consuming 71.6 mW. In real-world evaluations, including a user study with 28 participants, our system outperforms prior on-device models in speech quality and noise suppression, paving the way for next-generation intelligent wireless hearables that can enhance hearing entirely on-device.