EigenCircuit: Divergent Synthetic Benchmark Generation for Hardware Security Using PCA and Linear Programming

Abstract

Benchmarks are the standards by which technologies can be evaluated and fairly compared. In the field of digital circuits, benchmarks were critical for the development of CAD and FPGA tools decades ago. Hardware security is an emerging field of research where new techniques of security and vulnerability of hardware designs are being proposed in higher volume each year. Using decade-old VLSI/CAD-oriented benchmarks for analyzing the techniques has many issues as these benchmarks were not developed for security research. Additionally, the rise of statistical analysis or machine learning (ML) to model vulnerabilities and solve security issues demands a very large set of samples for training purposes. Since the number of available VLSI/CAD benchmarks is limited, such volume can only be obtained through synthetic benchmark generation tools. To accommodate both of these needs, the first hardware security-oriented synthetic circuit benchmark generation framework is developed in this article. With the use of principal component analysis (PCA) and linear optimization tool, the benchmarks generated by the proposed framework are “divergent,” that is having maximum variation in structures from each other. By accommodating user inputs for desired features, the framework offers customization for generating richer and more challenging benchmarks for data-driven hardware security. With thorough experimentation, we demonstrate our framework’s scalability, the structural and functional variations in the generated benchmarks, and the advantage of structurally variant synthetic benchmarks in hardware security applications.