Qier Ma, Richard George
Following a target speaker in a noisy environment, commonly known as the cocktail party problem, remains particularly challenging for cochlear implant (CI) users. This study aims to address this issue by presenting a resource-efficient ASIC designed for real-time EEG-based auditory attention decoding. The methodology involves integrating a quantized CNN inference engine with a Pearson-correlation classifier, and employing streaming execution, on-chip buffering, and memory-efficient dataflow to optimize resource use without sacrificing performance. The architectures have been implemented in GF22FDX 22-nm CMOS technology, with a total die size of 2.09 mm2, where the CNN inference engine occupies just 0.076 mm2. Operating at a core voltage of 0.55 V, the design achieves a remarkably low power consumption of 0.4941 mW and a latency of 7.34 ms. Results indicate that this ASIC provides an energy-efficient platform for EEG-based auditory attention decoding, contributing significant advancements for hearing-assistance applications, especially for CI users who struggle in multi-speaker environments.
@article{f2b97a2e-e236-4748-9fdf-809e9e7d8cd4,
title={A Resource-Efficient CNN-Based EEG Auditory Attention Decoding ASIC},
author={Qier Ma and Richard George},
year={2024},
language={en}
}TY - JOUR TI - A Resource-Efficient CNN-Based EEG Auditory Attention Decoding ASIC AU - Qier Ma AU - Richard George PY - 2024 LA - en ER -
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