WEARABLE TECH AND BIOMETRICS


How to manage size and power constraints when implementing AI/ML in wearable audio devices


By Sree Durbha, senior director of product marketing, Alif Semiconductor


C


ompact wearable audio devices such as earbuds and hearing aids are powered by microcontroller-based systems-on-chip (SoCs), which are marvels of engineering with their high integration. These SoCs integrate general purpose computing and high-performance digital signal processing (DSP) capabilities which handle crucial audio functions, such as codec processing, noise reduction, FFT analysis and compression, alongside Bluetooth network functionality, battery management and system control. This level of integration not only reduces the bill of materials but also enables manufacturers


to deliver superior audio quality in tiny, wearable formats.


However, the game is about to change. The next frontier for wearable audio is artificial intelligence (AI). By embedding AI directly into the SoC, manufacturers can dramatically enhance audio functions such as noise cancellation and echo cancellation, introducing advanced features such as speech recognition and natural language processing (NLP) for key word spotting (KWS).


Yet the challenge lies in implementing AI without compromising the compactness, power efficiency or cost of these devices.


Fig. 1: Benchmark performance of Alif Semiconductor’s Balletto wireless MCU with Cortex-M55 core, Helium M vector extension and Ethos-U55 NPU core. Comparison of performance for common audio processing functions: noise suppression, two-mic beamforming, echo cancellation and keyword spotting


42 APRIL 2025 | ELECTRONICS FOR ENGINEERS

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