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<p>Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder that remains difficult to cure. However, early screening and timely intervention can significantly slow its progression. Traditional AD detection methods are plagued by high misdiagnosis rates, low hardware integration, and lack of diagnostic diversity. To address these challenges, this paper proposes a configurable System-on-Chip (SoC) design based on a multimodal fusion Artificial Neural Network (ANN) for high-precision diagnosis. The proposed design integrates Electroencephalogram (EEG) and Magnetic Resonance Imaging (MRI) signals. First, a discretized reverse training method was employed to compress the features of the MRI images and reduce the input dimensionality. Second, intra-layer parallel computation and inter-layer pipeline scheduling were implemented to enhance the computational throughput. Finally, a dynamic configuration strategy for Processing Elements (PE) was introduced to optimize the hardware resource utilization. The proposed design achieves a six-fold improvement in throughput and provides multiple diagnostic approaches for AD. In conclusion, this work provides an efficient and scalable hardware solution for the early screening and dynamic monitoring of AD, which is expected to promote the development of portable and intelligent AD diagnostic devices and has good prospects for clinical transformation and application.</p>

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Design of a configurable SoC for Alzheimer’s disease detection based on multimodal signals

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月10日

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  • Yannan Yuan,
  • Liufang Sheng,
  • Zhikang Chen,
  • Yuejun Zhang,
  • Qikang Li,
  • Junping Chen,
  • Ke Ding,
  • Lei Shi,
  • Qiaoxia Hu,
  • Wenming He

摘要

Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder that remains difficult to cure. However, early screening and timely intervention can significantly slow its progression. Traditional AD detection methods are plagued by high misdiagnosis rates, low hardware integration, and lack of diagnostic diversity. To address these challenges, this paper proposes a configurable System-on-Chip (SoC) design based on a multimodal fusion Artificial Neural Network (ANN) for high-precision diagnosis. The proposed design integrates Electroencephalogram (EEG) and Magnetic Resonance Imaging (MRI) signals. First, a discretized reverse training method was employed to compress the features of the MRI images and reduce the input dimensionality. Second, intra-layer parallel computation and inter-layer pipeline scheduling were implemented to enhance the computational throughput. Finally, a dynamic configuration strategy for Processing Elements (PE) was introduced to optimize the hardware resource utilization. The proposed design achieves a six-fold improvement in throughput and provides multiple diagnostic approaches for AD. In conclusion, this work provides an efficient and scalable hardware solution for the early screening and dynamic monitoring of AD, which is expected to promote the development of portable and intelligent AD diagnostic devices and has good prospects for clinical transformation and application.