A Resource-Efficient CNN-Based EEG Auditory Attention Decoding ASIC

2026-08-20

AI Summary

本文提出了一种资源高效的基于CNN的EEG听觉注意力解码ASIC，通过量化CNN推理引擎和皮尔逊相关分类器解决嘈杂环境中助听问题。

Abstract

Following a target speaker in a noisy environment, commonly known as the cocktail party problem, remains particularly challenging for cochlear implant (CI) users. Recent studies have explored EEG-based auditory attention decoding (AAD) using neural networks to enhance hearing assistance. This paper presents a resource-efficient ASIC for real-time EEG-based auditory attention decoding by integrating a quantized CNN inference engine and a Pearson-correlation classifier. The proposed architecture employs streaming execution, on-chip buffering, and memory-efficient dataflow to reduce hardware cost while maintaining real-time performance. The proposed ASIC has been fully implemented in GF22FDX 22-nm CMOS technology, occupying a total silicon area of 2.09 mm$^2$(1264$μ$m x 1654$μ$m), with the CNN inference engine and streaming classification engine requiring only 0.076 mm$^2$. Operating at a core voltage of 0.55 V, the design achieves a power consumption of 0.4941 mW and an inference latency of 7.34 ms, providing an energy-efficient hardware platform for EEG-based auditory attention decoding in hearing-assistance applications.

Problem

• EEG
• auditory attention decoding
• cochlear implant
• hearing assistance
• noisy environment

Innovation

• Resource-Efficient ASIC
• Quantized CNN
• Pearson-Correlation Classifier
• Streaming Execution