一种基于资源高效型CNN的EEG听觉注意力解码ASIC
A Resource-Efficient CNN-Based EEG Auditory Attention Decoding ASIC
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中文总结 AI 辅助
针对人工耳蜗用户的鸡尾酒会问题，提出集成量化CNN与皮尔逊相关分类器的资源高效型ASIC，采用GF22FDX工艺实现，能效高，可用于实时EEG听觉注意力解码。
中文摘要 AI 辅助
在嘈杂环境中跟随目标说话者的任务，即所谓的鸡尾酒会问题，对于人工耳蜗（CI）用户而言仍然极具挑战性。近期研究探索了基于EEG的听觉注意力解码（AAD），利用神经网络来增强助听效果。本文提出一种资源高效型ASIC，用于实时基于EEG的听觉注意力解码，该芯片集成了量化CNN推理引擎和皮尔逊相关分类器。所提架构采用流式执行、片上缓冲以及内存高效的数据流，以降低硬件成本，同时保持实时性能。该ASIC已采用GF22FDX 22nm CMOS工艺完成全流程实现，总硅面积为2.09mm²（1264μm×1654μm），其中CNN推理引擎和流式分类引擎仅占用0.076mm²。在核心电压0.55V下，该设计功耗为0.4941mW，推理延迟为7.34ms，为助听应用中的基于EEG的听觉注意力解码提供了高能效的硬件平台。
英文摘要
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.
