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Soft errors caused by energetic particle strikes in on-chip cache memories have become a critical challenge for microprocessor design. Architectural vulnerability factor (AVF), which is defined as the probability that a transient fault in the structure would result in a visible error in the final output of a program, has been widely employed for accurate soft error rate estimation. Recent studies have found that designing soft error protection techniques with the awareness of AVF is greatly helpful to achieve a tradeoff between performance and reliability for several structures (i.e., issue queue, reorder buffer). Considering large on-chip L2 cache, redundancy-based protection techniques (such as ECC) have been widely employed for L2 cache data integrity with high costs. Protecting caches without accurate knowledge of the vulnerability characteristics may lead to the over-protection, thus incurring high overheads. Therefore, designing AVF-aware protection techniques would be attractive for designers to achieve a cost-efficient protection for caches, especially at early design stage. In this paper, we propose an improved AVF estimation framework for conducing comprehensive characterization of dynamic behavior and predictability of L2 cache vulnerability. We propose to employ Bayesian Additive Regression Trees (BART) method to accurately model the variation of L2 cache AVF and to quantitatively explain the important effects of several key performance metrics on L2 cache AVF. Then we employ bump hunting technique to extract some simple selecting rules based on several key performance metrics for a simplified and fast estimation of L2 cache AVF. Using the simplified L2 cache AVF estimator, we develop an AVF-aware ECC technique as an example to demonstrate the cost-efficient advantages of the AVF prediction based dynamic fault tolerant techniques. Experimental results show that compared with traditional full ECC technique, AVF-aware ECC technique reduces the L2 cache access latency by 16.5% and saves power consumption by 11.4% for SPEC2K benchmarks averagely.

Accurate and Simplified Prediction of L2 Cache Vulnerability for Cost-Efficient Soft Error Protection