Adaptable and divergent synthetic benchmark generation for hardware security

Abstract

Benchmarking can drive the development of technologies by facilitating standardization of features for comparison of different methods. While hardware security has seen an exponential growth in innovation throughout the last decade, the lack of sufficient benchmarks for data-driven analysis is prominent. Researchers must currently rely on decades-old VLSI benchmarks, which in most cases were not designed with security evaluation in mind. Considering the present day computational power, these benchmarks lack in both quality and quantity for usage in hardware security topics such as obfuscation and hardware Trojans. Many advanced techniques, like statistical analysis and machine learning, require a large number of samples in order to sufficiently examine the feature space. In an attempt to resolve this issue, we have developed the first synthetic benchmark generation process flow. This paper describes our novel technique that utilizes linear optimization to generate an endless number of synthetic combinational benchmarks that are adaptable to user input constraints and divergent in quantifiable structural features from input reference benchmarks. Thus, our framework offers customization for generating richer and more challenging benchmarks for data-driven hardware security. Through experimentation, we verify that our benchmarks offers more structural variation than the current benchmark suites.