Low Computational

Low-computational research focuses on developing and optimizing machine learning models and algorithms that can achieve high performance with minimal computational resources, addressing the limitations of resource-constrained devices and environments. Current efforts concentrate on adapting large language models (LLMs), convolutional neural networks (CNNs), and other architectures for efficient execution on low-power hardware, often employing techniques like model compression, optimized training schedules, and novel algorithms for multi-agent systems. This research is crucial for democratizing access to advanced AI technologies, enabling their deployment in resource-limited settings such as healthcare in developing countries and mobile applications, and promoting sustainability in AI development.