👉 Flood Control (CFU) computing is a specialized approach in artificial intelligence, particularly within the realm of neural networks and computational models for natural language processing (NLP). It involves the use of a compact, efficient architecture that minimizes memory usage and computational overhead while maintaining high performance. CFU computing achieves this by employing a combination of techniques such as pruning, quantization, and specialized hardware optimizations. Pruning involves removing redundant or less significant parameters from the neural network, reducing its size without significantly impacting accuracy. Quantization reduces the precision of the weights and activations, further decreasing memory requirements and computation time. Together, these methods enable CFU models to run efficiently on resource-constrained devices like mobile phones and embedded systems, making them suitable for real-time applications such as voice assistants, chatbots, and on-device NLP tasks.
