Optimized Multiple-Bit-Flip Soft-Errors-Tolerant TCAM using Machine Learning

Soft errors from radiations can change the data in electronic devices especially memory cells such as in TCAMs. The soft errors cause bit-flip errors that makes the data are corrupted in the network. This paper presents a novel machine learning for a multiple-bit-flip-tolerant TCAM that address soft errors problem using partial don't-care keys (X-keys). The general methodology is classified into two steps, i.e., statistical training and X-keys matching. First, we train the machine by collecting match probability of a filter by using X-keys that match the same locations as the search key. This method uses statistical training to determine the most efficient of number of don't cares. Moreover, in the statistical training, we also explore the maximum number of don't cares that produce best performance in covering the soft errors. Finally, the X-keys are implemented in the TCAM to correct bit-flip errors. The suitable number of don't cares in X-key is determined from the distribution of match probability of the X-keys so that the best degree of tolerance of the TCAM against soft errors is found. Match probabilities for various filters are shown. Experimental results demonstrate that the soft-error tolerance using statistical data has better soft-error tolerance than other methods. The proposed method is useful for soft-error tolerant TCAMs in routers and firewalls for robust networks.