Summary of Evaluation Of Bio-inspired Models Under Different Learning Settings For Energy Efficiency in Network Traffic Prediction, by Theodoros Tsiolakis et al.
Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic Prediction
by Theodoros Tsiolakis, Nikolaos Pavlidis, Vasileios Perifanis, Pavlos Efraimidis
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning algorithms have emerged as powerful tools for handling large datasets and providing accurate predictions in cellular traffic forecasting. However, their environmental impact, particularly energy consumption, is often overlooked. This study investigates the potential of two bio-inspired models, Spiking Neural Networks (SNNs) and Reservoir Computing through Echo State Networks (ESNs), for cellular traffic forecasting. The evaluation focuses on both predictive performance and energy efficiency in centralized and federated settings. Traditional architectures such as Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs) are also compared to provide a comprehensive evaluation. Using data collected from three locations in Barcelona, Spain, the results indicate that bio-inspired models can achieve significant energy savings while maintaining predictive accuracy comparable to traditional architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cellular traffic forecasting helps network operators manage resources and detect anomalies. But processing big datasets uses a lot of energy! This study looks at two special kinds of artificial intelligence (AI) called Spiking Neural Networks (SNNs) and Reservoir Computing through Echo State Networks (ESNs). These models are designed to work like the human brain and use less energy. The researchers tested these models in different settings and compared them to other types of AI, like Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs). They used data from three places in Barcelona, Spain, and found that the bio-inspired models can save a lot of energy while still being accurate. |
Keywords
» Artificial intelligence » Machine learning
