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—We present HYBRO, an automatic methodology to generate high coverage input vectors for Register Transfer Level (RTL) designs based on branch-coverage directed approach. HYBRO uses dynamic simulation data and static analysis of RTL control flow graphs (CFGs). A concrete simulation is applied over a fixed number of cycles. Instrumented code records the(More)
OBJECTIVE Polymorphisms in the adiponectin gene (ADIPOQ) have been associated with type 2 diabetes and diabetic nephropathy in type 1 diabetes, in mostly European-derived populations. RESEARCH DESIGN AND METHODS A comprehensive association analysis of 24 single-nucleotide polymorphisms (SNPs) in the adiponectin gene was performed for type 2 diabetes and(More)
We present a methodology to generate input stimulus for design validation using GoldMine, an automatic assertion generation engine that uses data mining and formal verification. GoldMine mines the simulation traces of a behavioral Register Transfer Level (RTL) design using a decision tree based learning algorithm to produce candidate assertions. These(More)
Automatic assertion generation methodologies based on machine learning generate assertions at bit level. These bit level assertions are numerous, making them unreadable and frequently unusable. We propose a methodology to discover word level features using static and dynamic analysis of the RTL source code. We use discovered word level features for the(More)
We enhance STAR, an automatic technique for functional input vector generation for design validation. STAR statically analyzes the source code of the Register-Transfer Level (RTL) design. The STAR approach is a hybrid between RTL symbolic execution and concrete simulation that offsets the disadvantages of both. The symbolic execution, which follows the(More)
Diagnosing performance violations is one of the biggest challenges in transaction level modeling of systems. In this paper, we propose a methodology to localize root causes of latency or throughput violations. We present a concurrent pattern mining approach to infer frequent patterns from transaction traces to localize root causes. We apply three categories(More)