An integrated and automated memory optimization flow for FPGA behavioral synthesis

摘要

Behavioral synthesis tools have made significant progress in compiling high-level programs into register-transfer level (RTL) specifications. But manually rewriting code is still necessary in order to obtain better quality of results in memory system optimization. In recent years different automated memory optimization techniques have been proposed and implemented, such as data reuse and memory partitioning, but the problem of integrating these techniques into an applicable flow to obtain a better performance has become a challenge. In this paper we integrate data reuse, loop pipelining, memory partitioning, and memory merging into an automated optimization flow (AMO) for FPGA behavioral synthesis. We develop memory padding to help in the memory partitioning of indices with modulo operations. Experimental results on Xilinx Virtex-6 FPGAs show that our integrated approach can gain an average 5.8× throughput and 4.55× latency improvement compared to the approach without memory partitioning. Moreover, memory merging saves up to 44.32% of block RAM (BRAM).