Performance Estimation for the Exploration of CPU-Accelerator Architectures

In this paper we present an approach for studying the design space when interfacing reconfigurable accelerators with a CPU. For this purpose we introduce a framework based on the LLVM infrastructure that performs hardware/software partitioning with runtime estimation utilizing profiling information and code analysis. We apply it to reconfigurable accelerators that are controlled by a CPU via a direct low-latency interface but also have direct access to the memory hierarchy. Our results show that a shared L2 cache for CPU and accelerator seems to be the most promising design point for a range of applications.