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https://github.com/muhammadsaadhsn/neural-network-architecture-os-concepts

This project entails designing an OS to implement a neural network on a multi-core processor. The OS uses processes for network layers and threads for neurons, employs IPC through pipes, and ensures efficient computation via parallel processing, process scheduling, and memory management.
https://github.com/muhammadsaadhsn/neural-network-architecture-os-concepts

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This project entails designing an OS to implement a neural network on a multi-core processor. The OS uses processes for network layers and threads for neurons, employs IPC through pipes, and ensures efficient computation via parallel processing, process scheduling, and memory management.

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# Neural-Network-Architecture-OS-Concepts

An operating system that implements a neural network architecture using separate

processes and threads on a multi-core processor. The system uses inter-process communication

through pipes for exchanging information such as weights and biases between processes. Each layer of

the neural network should be represented as a separate process, and each neuron within a layer should

be treated as a separate thread. During backpropagation, the error signal is propagated backward

through the layers of the network while utilizing the processing power of multiple cores.



## Detailed Description

Neural networks are a popular machine learning technique that mimics the structure and function of the

human brain to solve complex problems. A neural network consists of interconnected layers of neurons,

where each neuron takes input from multiple sources, processes it, and produces an output signal.



In a multi-core processor, there are several processing units (cores) that can execute multiple threads

simultaneously, thereby increasing the system's overall processing power. To take advantage of this

capability, the proposed operating system should utilize multi-core processing to parallelize the

computation of the neural network.



Each layer of the neural network should be represented as a separate process, and each neuron within a

layer should be treated as a separate thread. These threads can be assigned to different cores of the

processor to enable parallel processing of the inputs.



The operating system should provide a mechanism for inter-process communication (IPC) through pipes

to exchange information between processes. Weights and biases, which are the parameters that the

neural network learns during training, should be exchanged through these pipes.



During training, the system should use a batch-based approach, where the input data is divided into

smaller batches and each batch is processed by a separate layer in the network. Each layer should receive

its input batch from the previous layer through a pipe and then apply the weights and biases to generate

the output. The output should be passed to the next layer as input through another pipe.



During backpropagation, the error signal should be propagated backward through the layers of the

network, and the system should update the weights and biases based on the calculated gradients. You

could also use shared memory for backward propagation of weights and bias. To utilize the processing

power of multiple cores during backpropagation, the system should divide the computation into smaller

tasks and assign each task to a different core.



Furthermore, backpropagation includes:

* Process Synchronization: The system should ensure that only one process accesses the shared

resources such as weights and biases at any given time to avoid race conditions. This can be

achieved using synchronization primitives such as semaphores or locks.

* Memory Management: The operating system should provide a mechanism for allocating and

deallocating memory for the neural network processes and threads. The system should also

ensure that each process and thread has access to its own memory space to avoid conflicts.

* Inter-Process Communication: The operating system should provide a mechanism for inter-

process communication (IPC) through pipes or other forms of communication to exchange 

information such as weights and biases between processes during backpropagation.

* Thread Management: The system should manage threads efficiently to allow parallel processing

of inputs and reduce the overall training time of the neural network.

* Process Scheduling: The operating system should schedule processes and threads efficiently to

ensure that the neural network training process runs smoothly and efficiently.