👉 LTD (Limited Depth) computing is an architectural paradigm in artificial intelligence and machine learning that focuses on reducing the computational complexity of deep neural networks by limiting their depth, or the number of layers. Traditional deep learning models often require vast amounts of data and computational resources to achieve high performance, but LTD aims to create more efficient models that can still achieve competitive results with fewer parameters and layers. This is accomplished through techniques such as pruning (removing redundant connections), quantization (reducing the precision of weights and activations), and knowledge distillation (training a smaller model to mimic a larger, more complex one). By simplifying the network structure, LTD not only reduces memory and energy consumption but also makes models more deployable on resource-constrained devices, such as smartphones and edge computing systems. This approach is particularly valuable in applications where computational efficiency and real-time performance are critical, such as autonomous vehicles, wearable devices, and IoT systems.
