Optimizing Energy Efficiency

Combining theoretical modeling and experiments to reduce energy consumption during machine learning training.

Innovative Research in Energy Efficiency

At qqasa, we focus on optimizing energy consumption in model training through theoretical modeling and experimental validation, leveraging thermodynamics and information theory for sustainable AI development.

Solar panels are installed on a rooftop under a partly cloudy sky, capturing sunlight efficiently. The panels are aligned in neat rows, suggesting a modern approach to renewable energy.

Our Mission

Our Approach

We design algorithms that dynamically adjust training parameters, validating their performance on public datasets to enhance efficiency and reduce energy usage in machine learning processes.

Energy Optimization

Innovative approach to reduce energy consumption during model training.

A solar panel is positioned on a large rock near a body of water, suggesting energy harnessing in a natural setting.

Entropy Framework

Analyzing energy consumption through entropy production rate dynamics.

A series of solar panels arranged in rows, capturing sunlight. The panels have a grid-like pattern and are placed on a dark, gravel-covered surface. The symmetry and alignment of the panels give a sense of order and efficiency.

Rows of solar panels are arranged in a diagonal pattern across a green field. The panels are uniformly spaced and cover a large area, emphasizing renewable energy and sustainability.

Public Datasets

Validation experiments using popular datasets and simulation tools.

Research

Optimizing energy consumption through innovative algorithms.