We study neural networks (NNs) operating under hardware-induced memory faults and computational errors, which can degrade reliability in safety-critical applications. To address this challenge, we propose a novel real-valued error correction coding (ECC) framework capable of detecting and correcting both types of errors. Our approach imposes structured real-valued linear constraints on network weights, enabling efficient error detection and recovery without increasing the number of parameters or compromising classification performance. The proposed method leverages linear programming and sparse recovery techniques to achieve low-complexity error correction. To the best of our knowledge, this is the first real-number-based ECC scheme that simultaneously addresses memory and computational errors in neural networks.

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