Error detection in digital neural networks: an algorithmic-based approach for inner product protection

Artificial Neural Networks are an interesting solution for several real-time applications in the area of signal and image processing, in particular since recent advances in VLSI integration technologies allow for efficient hardware realizations. The use of dedicated circuits implementing the neural networks in mission-critical applications requires a high level of protection with respect to errors due to faults to guarantee output credibility and system availability. In this paper, the problem of concurrent error detection in dedicated neural networks is discussed by adopting an algorithm-based approach to check the inner product, i.e., the most of the computation performed in the neural network. Effectiveness and efficiency of this technique is shown and evaluated for the widely-used classes of neural paradigms.