Feature based similarity search with application to speedpath analysis

Abstract

In test and diagnosis, one often runs into the situation that after analyzing a set of samples, a few of these samples are identified as being “special”. Then, in a large population of samples one desires to identify all samples that are “similar” to the special samples. The process is called a similarity search. This paper presents a feature based similarity search approach and discusses three potential methods to implement this approach. These methods are (1) building a model to capture the characteristics of the non-special samples, (2) building a model to capture the characteristics of the special samples, and (3) searching for the hypotheses to explain individually why each sample is special. We apply similarity search to the speedpath analysis problem where special samples are special paths that limit the performance of silicon chips. The goal is to identify more paths in the design with similar characteristics to the speedpaths. The effectivenesses of the three methods are analyzed based on speedpath data collected from a high-performance microprocessor.