FPGA Implementation of an Artificial Intelligence Signal Recognition System
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Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.
Abstract
Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.
참고문헌 (9)
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- A. Catalani, "Arrhythmia Classification from ECG signals", Master, pp.2019
- A. R. Young, "A Review of Spiking Neuromorphic Hardware Communication System", IEEE Access, 7, pp.135606~135620 (2019)
- Y. Dorogyy, "Designing spiking neural networks", pp.124~127 (2016), IEEE TCSET
- H. Jang, "An Introduction to Spiking Neural Networks : Probabilistic Models, Learning Rules, and Applications", IEEE Signal Processing Magazine, 36(6), pp.64~77 (2019)
- W. Gerstner, "Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition" (2014), Cambridge University Press