Abstract
Stencil computation is one of the most important kernels in many application domains such as image processing, solving partial diferential equations, and cellular automata. Many of the stencil kernels are complex, usually consist of multiple stages or iterations, and are often computation-bounded. Such kernels are often off-loaded to FPGAs to take advantages of the efficiency of dedicated hardware. However, implementing such complex kernels efficiently is not trivial, due to complicated data dependencies, difficulties of programming FPGAs with RTL, as well as large design space. In this paper we present SODA, an automated framework for implementing Stencil algorithms with Optimized Datalow Architecture on FPGAs. The SODA microarchitecture minimizes the on-chip reuse bufer size required by full data reuse and provides flexible and scalable fine-grained parallelism. The SODA automation framework takes high-level user input and generates efficient, high-frequency datalow implementation. This significantly reduces the difficulty of programming FPGAs efficiently for stencil algorithms. The SODA design-space exploration framework models the resource constraints and searches for the performance-optimized coniguration with accurate models for post-synthesis resource utilization and on-board execution throughput. Experimental results from on-board execution using a wide range of benchmarks show up to 3.28x speed up over 24-thread CPU and our fully automated framework achieves better performance compared with manually designed state-of-the-art FPGA accelerators.
Peipei Zhou
Assistant Professor of Engineering
My research interests include Customized Computer Architecture and Programming Abstraction for Health & AI Applications