Artificial intelligence is a swiftly growing field, and many are researching whether AI can serve as a diagnostic aid in the medical domain. However, the primary weakness of traditional machine learning for many applications is energy efficiency, and this may hamper its ability to be effectively utilized in medicine for portable or edge systems. In order to be more effective, new energy-efficient machine learning paradigms must be investigated for medical applications. In addition, smaller models with fewer parameters would be better suited to medical edge systems. By processing data as a series of "spikes" instead of continuous values, spiking neural networks (SNN) may be the right model architecture to address these concerns. This work investigates the proposed advantages of SNNs compared to more traditional architectures when tested on various medical datasets. We compare the energy efficiency of SNN and recurrent neural network (RNN) solutions by finding sizes of each architecture that achieve similar accuracy. The energy consumption of each comparable network is assessed using standard tools for such evaluation.
On the SEED human emotion dataset, SNN architectures achieved up to 20x lower energy per inference than an RNN while maintaining similar classification accuracy. SNNs also achieved 30x lower energy consumption on the PTB-XL ECG dataset with similar classification accuracy. These results show that spiking neural networks are more energy efficient than traditional machine learning models at inference time while maintaining a similar level of accuracy for various medical classification tasks. With this superior energy efficiency, this makes it possible for medical SNNs to operate on edge and portable systems. / Master of Science / As artificial intelligence grows in popularity, especially with the rise of new large language models like Chat-GPT, a weakness in traditional architectures becomes more pronounced.
These AI models require ever-increasing amounts of energy to operate. Thus, there is a need for more energy-efficient AI models, such as the spiking neural network (SNN). In SNNs, information is processed in a series of spiking signals, like the biological brain. This allows the resulting architecture to be highly energy efficient and adapted to processing time-series data.
A domain that often encounters time-series data and would benefit from greater energy efficiency is medicine. This work seeks to investigate the proposed advantages of spiking neural networks when applied to the various classification tasks in the medical domain.
Specifically, both an SNN and a traditional recurrent neural network (RNN) were trained on medical datasets for brain signal and heart signal classification. Sizes of each architecture were found that achieved similar classification accuracy and the energy consumption of each comparable network was assessed. For the SEED brain signal dataset, the SNN achieved similar classification accuracy to the RNN while consuming as little as 5% of the energy per inference. Similarly, the SNN consumed 30x less energy than the RNN while classifying the PTB-XL ECG dataset. These results show that the SNN architecture is a more energy efficient model than traditional RNNs for various medical tasks at inference time and may serve as the solution to the energy consumption problem of medical AI.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/121027 |
| Date | 27 August 2024 |
| Creators | Smith IV, Lyle Clifford |
| Contributors | Electrical and Computer Engineering, Jones, Creed Farris, Ha, Dong S., Yi, Yang |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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