This project implements a neural network using separate processes and threads on a multi-core processor. It uses inter-process communication (IPC) with pipes to exchange information between processes. Each neural network layer is a separate process, and each neuron within a layer is a separate thread. This approach leverages multi-core processing to parallelize computations.
- Parallel Processing: Uses multi-core processors to speed up computations.
- Inter-Process Communication (IPC): Utilizes pipes for efficient data exchange between processes.
- Batch Processing: Processes input data in batches for efficiency.
- Backpropagation and Weight Update: [To be implemented] Includes backpropagation for gradient calculation and updating weights/biases.
- Synchronization and Resource Management: Ensures thread safety with proper synchronization and resource allocation. Uses semaphores or locks to control access to shared resources.
- Scalability and Optimization: Enhances scalability and performance through optimized resource usage. Efficiently schedules processes and threads to optimize neural network training.
- Error Handling and Stability: Provides robust error handling for stability and reliability.
- Process Management: Each neural network layer is a separate process, with OS handling creation, termination, and synchronization.
- Thread Management: Each neuron is a separate thread, managed by the OS for parallel processing and resource sharing.
- Memory Management: Provides mechanisms for memory allocation and deallocation for processes and threads.
Neural networks mimic the human brain to solve complex problems. By using multiple cores on a processor, we can run multiple threads simultaneously, enhancing processing power. The goal is to design a system that uses this capability to parallelize neural network computations.
- Each neural network layer is a separate process; each neuron in a layer is a separate thread.
- Use IPC to exchange weights and biases between processes.
- Process input data in batches during training.
- Employ locking mechanisms to prevent simultaneous resource access by multiple threads.
- Implement backpropagation to update weights and biases based on error signals.
- Efficiently handle process synchronization, memory management, IPC, thread management, and process scheduling.
This project uses system calls and libraries common in OS labs, such as fork()
, wait()
, pipes, the pthread library, mutexes, and semaphores.