Exploiting FPGA Capabilities for Accelerated Biomedical Computing
26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023, Poznan, Polonya, 20 - 22 Eylül 2023, cilt.2023-September, ss.48-53, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 2023-September
- Doi Numarası: 10.23919/spa59660.2023.10274450
- Basıldığı Şehir: Poznan
- Basıldığı Ülke: Polonya
- Sayfa Sayıları: ss.48-53
- Anahtar Kelimeler: Biomedical Applications, ECG Signal Analysis, FPGA Implementation, Hardware Acceleration
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Erciyes Üniversitesi Adresli: Evet
Özet
This study presents advanced neural network architectures including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTMs), and Deep Belief Networks (DBNs) for enhanced ECG signal analysis using Field Programmable Gate Arrays (FPGAs). We utilize the MIT-BIH Arrhythmia Database for training and validation, introducing Gaussian noise to improve algorithm robustness. The implemented models feature various layers for distinct processing and classification tasks and techniques like EarlyStopping callback and Dropout layer are used to mitigate overfitting. Our work also explores the development of a custom Tensor Compute Unit (TCU) accelerator for the PYNQ Z1 board, offering comprehensive steps for FPGA-based machine learning, including setting up the Tensil toolchain in Docker, selecting architecture, configuring PS-PL, and compiling and executing models. Performance metrics such as latency and throughput are calculated for practical insights, demonstrating the potential of FPGAs in high-performance biomedical computing. The study ultimately offers a guide for optimizing neural network performance on FPGAs for various applications.