Description: Layered architecture is a design approach that organizes the functions of a system into distinct layers, each responsible for a specific set of tasks. This modular model allows each layer to interact with adjacent ones, facilitating communication and managing complexities. In the context of neural networks, for example, layers can represent different levels of data processing, where each layer progressively transforms the input. In recurrent neural networks, layered architecture enables handling sequences of data, where layers can remember information from previous inputs. In the realm of computer networks, this approach is reflected in network reference models, which divide communication functions into layers such as the application layer, transport layer, and network layer, among others. This separation of functions not only enhances modularity and component reuse but also simplifies the maintenance and updating of complex systems. Layered architecture has become a fundamental principle in software and system design, promoting scalability and flexibility in technological development.
History: Layered architecture has its roots in the evolution of computing systems and networks throughout the 20th century. One of the most significant milestones was the development of the OSI (Open Systems Interconnection) model in the 1980s, which defined a seven-layer framework for communication in networks. Although the OSI model was not universally adopted, it influenced the creation of the TCP/IP model, which became the foundation of the Internet. In the field of artificial intelligence, neural networks began to gain popularity in the 1980s, and their layered design was formalized with the development of backpropagation algorithms, allowing for the creation of deeper and more complex networks.
Uses: Layered architecture is used in various areas of technology, including software development, computer networks, and artificial intelligence systems. In software development, it allows for the creation of modular applications where each layer can be developed and maintained independently. In networks, it facilitates interoperability between different systems and protocols, allowing data to flow efficiently through the layers. In artificial intelligence, layered neural networks are fundamental to deep learning, where each layer progressively extracts more complex features from input data.
Examples: Examples of layered architecture include the TCP/IP model, which organizes network functions into layers such as the application layer and transport layer. In the realm of neural networks, an example is the convolutional neural network (CNN), which uses multiple layers to process images, where each layer detects specific features such as edges, textures, and patterns. Another example is the development of web applications, where layers such as the presentation layer, business logic layer, and data access layer are used to structure the code efficiently.
