Accelerating Legacy String Kernels via Bounded Automata Learning
Kevin Angstadt, Jean-Baptiste Jeannin, Westley R. Weimer · 2020
The adoption of hardware accelerators, such as FPGAs, into general-purpose computation pipelines continues to rise, but programming models for these devices lag far behind their CPU counterparts. Legacy programs must often be rewritten at very low levels of abstraction, requiring intimate knowledge of the target accelerator architecture. While techniques such as high-level synthesis can help port some legacy software, many programs perform poorly without manual, architecture-specific optimization.