Resource Constrained

Resource-constrained computing focuses on developing efficient algorithms and models for applications with limited computational resources, memory, and power, such as embedded systems and mobile robots. Current research emphasizes lightweight model architectures (e.g., smaller convolutional neural networks, quantized models, pruned transformers) and techniques like knowledge distillation, federated learning, and efficient hyperparameter optimization to improve performance while minimizing resource demands. This field is crucial for deploying AI and machine learning in resource-limited environments, enabling applications in areas like robotics, healthcare diagnostics, and IoT devices where full computational power is unavailable.