Resource-efficient Machine Learning

Machine Learning (ML) algorithms have demonstrated remarkable advancements across diverse fields, evolving to become more intricate and data-intensive.  This evolution is particularly driven by the expanding size of datasets and the infinite growing nature of data streams. However, this substantial progress has come at the cost of intensified energy consumption, emphasizing the urgent requirement for resource-efficient methodologies.  It is thus crucial to balance computational demands and model performance to mitigate the escalating environmental impact associated with the energy-intensive nature of machine learning processes.

Enhancing data efficiency stands as a central strategy in SEDIMARK to manage the considerable energy needs inherent in machine learning algorithms. SEDIMARK aims to achieve resource and energy efficiency during the training of ML models by reducing the quantity of data needed without compromising performance. To accomplish this, SEDIMARK will use summarization techniques in conjunction with ML algorithms, including but not limited to dimension reduction, sampling, and other reduction strategies.

In the SEDIMARK AI pipeline, dimension reduction techniques play a crucial role in mitigating resource consumption. By reducing the number of features, both computational complexity and memory requirements can be substantially lowered. Furthermore, the removal of irrelevant features through this process can enhance overall quality performance. Two main strategies that exist within dimension reduction are feature selection and feature extraction. The former involves the selection of a subset of the input features, while the latter entails constructing a new set of features in a lower-dimensional space from a given set of input features. This dual approach ensures a nuanced and effective reduction in the data footprint, contributing significantly to the overall goal of resource and energy efficiency in SEDIMARK.

Sampling is another effective strategy for resource-efficient machine learning. Instead of analyzing the entire dataset or maintaining a whole data stream, algorithms operate on a representative subset (or a sliding window for data streams). This approach is particularly useful for large datasets where processing the entire set is impractical.

Resource-efficient machine learning is not just a practical necessity but a crucial avenue for sustainable and scalable model development. By strategically employing dimension reduction, sampling, corsets, data distillation and other summarization techniques, the ML models will be computationally frugal, making them particularly suitable for deployment on devices with limited processing capabilities, such as edge and IoT devices. SEDIMARK can strike a balance between computational efficiency and model accuracy. As machine learning evolves, these optimization strategies will play an increasingly vital role in ensuring that advanced algorithms remain accessible and practical in real-world applications.