{"id":25164334,"url":"https://github.com/msaadx/Neural-Network-Architecture-OS-Concepts","last_synced_at":"2025-10-24T02:30:53.166Z","repository":{"id":174348375,"uuid":"652109225","full_name":"muhammadsaadhsn/Neural-Network-Architecture-OS-Concepts","owner":"muhammadsaadhsn","description":"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.","archived":false,"fork":false,"pushed_at":"2023-06-11T05:44:55.000Z","size":9,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-28T15:39:51.083Z","etag":null,"topics":["fork","neural-network","operating-systems","processors"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/muhammadsaadhsn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-06-11T05:40:15.000Z","updated_at":"2024-06-11T03:26:53.000Z","dependencies_parsed_at":"2023-07-03T12:31:38.585Z","dependency_job_id":null,"html_url":"https://github.com/muhammadsaadhsn/Neural-Network-Architecture-OS-Concepts","commit_stats":null,"previous_names":["muhammadsaadhasan/neural-network-architecture-os-concepts","muhammadsaadhsn/neural-network-architecture-os-concepts"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/muhammadsaadhsn%2FNeural-Network-Architecture-OS-Concepts","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/muhammadsaadhsn%2FNeural-Network-Architecture-OS-Concepts/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/muhammadsaadhsn%2FNeural-Network-Architecture-OS-Concepts/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/muhammadsaadhsn%2FNeural-Network-Architecture-OS-Concepts/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/muhammadsaadhsn","download_url":"https://codeload.github.com/muhammadsaadhsn/Neural-Network-Architecture-OS-Concepts/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":237905529,"owners_count":19385037,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["fork","neural-network","operating-systems","processors"],"created_at":"2025-02-09T04:30:01.590Z","updated_at":"2025-10-24T02:30:52.870Z","avatar_url":"https://github.com/muhammadsaadhsn.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Neural-Network-Architecture-OS-Concepts\nAn operating system that implements a neural network architecture using separate\nprocesses and threads on a multi-core processor. The system uses inter-process communication\nthrough pipes for exchanging information such as weights and biases between processes. Each layer of\nthe neural network should be represented as a separate process, and each neuron within a layer should\nbe treated as a separate thread. During backpropagation, the error signal is propagated backward\nthrough the layers of the network while utilizing the processing power of multiple cores.\n\n## Detailed Description\nNeural networks are a popular machine learning technique that mimics the structure and function of the\nhuman brain to solve complex problems. A neural network consists of interconnected layers of neurons,\nwhere each neuron takes input from multiple sources, processes it, and produces an output signal.\n\nIn a multi-core processor, there are several processing units (cores) that can execute multiple threads\nsimultaneously, thereby increasing the system's overall processing power. To take advantage of this\ncapability, the proposed operating system should utilize multi-core processing to parallelize the\ncomputation of the neural network.\n\nEach layer of the neural network should be represented as a separate process, and each neuron within a\nlayer should be treated as a separate thread. These threads can be assigned to different cores of the\nprocessor to enable parallel processing of the inputs.\n\nThe operating system should provide a mechanism for inter-process communication (IPC) through pipes\nto exchange information between processes. Weights and biases, which are the parameters that the\nneural network learns during training, should be exchanged through these pipes.\n\nDuring training, the system should use a batch-based approach, where the input data is divided into\nsmaller batches and each batch is processed by a separate layer in the network. Each layer should receive\nits input batch from the previous layer through a pipe and then apply the weights and biases to generate\nthe output. The output should be passed to the next layer as input through another pipe.\n\nDuring backpropagation, the error signal should be propagated backward through the layers of the\nnetwork, and the system should update the weights and biases based on the calculated gradients. You\ncould also use shared memory for backward propagation of weights and bias. To utilize the processing\npower of multiple cores during backpropagation, the system should divide the computation into smaller\ntasks and assign each task to a different core.\n\nFurthermore, backpropagation includes:\n* Process Synchronization: The system should ensure that only one process accesses the shared\nresources such as weights and biases at any given time to avoid race conditions. This can be\nachieved using synchronization primitives such as semaphores or locks.\n* Memory Management: The operating system should provide a mechanism for allocating and\ndeallocating memory for the neural network processes and threads. The system should also\nensure that each process and thread has access to its own memory space to avoid conflicts.\n* Inter-Process Communication: The operating system should provide a mechanism for inter-\nprocess communication (IPC) through pipes or other forms of communication to exchange \ninformation such as weights and biases between processes during backpropagation.\n* Thread Management: The system should manage threads efficiently to allow parallel processing\nof inputs and reduce the overall training time of the neural network.\n* Process Scheduling: The operating system should schedule processes and threads efficiently to\nensure that the neural network training process runs smoothly and efficiently.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmsaadx%2FNeural-Network-Architecture-OS-Concepts","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmsaadx%2FNeural-Network-Architecture-OS-Concepts","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmsaadx%2FNeural-Network-Architecture-OS-Concepts/lists"}