{"ID":2833032,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.04867","arxiv_id":"2512.04867","title":"Functional Stability of Software-Hardware Neural Network Implementation The NeuroComp Project","abstract":"This paper presents an innovative approach to ensuring functional stability of neural networks through hardware redundancy at the individual neuron level. Unlike the classical Dropout method, which is used during training for regularization purposes, the proposed system ensures resilience to hardware failures during network operation. Each neuron is implemented on a separate microcomputer (ESP32), allowing the system to continue functioning even when individual computational nodes fail.","short_abstract":"This paper presents an innovative approach to ensuring functional stability of neural networks through hardware redundancy at the individual neuron level. Unlike the classical Dropout method, which is used during training for regularization purposes, the proposed system ensures resilience to hardware failures during ne...","url_abs":"https://arxiv.org/abs/2512.04867","url_pdf":"https://arxiv.org/pdf/2512.04867v1","authors":"[\"Bychkov Oleksii\",\"Senysh Taras\"]","published":"2025-12-04T14:49:57Z","proceeding":"cs.AR","tasks":"[\"cs.AR\",\"cs.NE\"]","methods":"[]","has_code":false}