Application Taxonomy via Algorithmic Commonality for Domain-Specific Architecture Desgin

In this paper, we propose an approach of application taxonomy from a perspective of algorithmic commonality. The taxonomy exploits algorithm-inherent characterization to imply a categorization of domain-specific architecture in the initial phase of architecture design. First, we introduce both metrics and graph-based mining algorithm to evaluate the commonality across multiple applications. Second, we present taxonomy algorithm to categorize applications into different specializations, which will tremendously reduce design complexity of accelerator-rich architectures. Finally, with benchmark suits of MiBench, PolyBench and SD-VBS, our methodology is validated and offers designers meaningful insight and direct illustration to assist the specialized architecture design.