Time-redundant multiple computation for fault-tolerant digital neural networks

In mission-critical applications of artificial neural networks, error correction at the architectural level is often mandatory to guarantee consistency and reliability of the network's outputs. Time redundancy allows for fault tolerance with low circuit complexity overhead. In this paper, the application REcomputing with Triplication With Voting (RETWV) at the system level is proposed for concurrent error correction in neural networks. Feed-forward multi-layered neural networks are considered as an example, but the proposed technique can be easily extended to different neural paradigms.