Superoptimized Memory Subsystems for Streaming Applications

Abstract

Because main memory is many times slower than modern processor cores, deep, multi-level cache hierarchies are ubiquitous in computers today. Similarly, applications deployed on ASICs and FPGAs are often hindered by slow external memories. Therefore, to achieve good performance, hardware designers must optimize main memory usage. Unfortunately, this process is often labor intensive and fails to explore the full range of potential memory designs. To address this issue for applications expressed in a streaming manner, we show that it is possible to generate automatically a superoptimized memory subsystem that can be deployed on an FPGA such that it performs better than a general-purpose memory subsystem. Rather than explore only simple memory subsystems, our superoptimizer is capable of exploring extremely complex designs consisting of multi-level caches and other components. Finally, we show that it is possible to deploy applications with superoptimized memory subsystems with minimal additional effort while achieving significant performance improvements over a naive memory subsystem.