SPINE: From C loop-nests to highly efficient accelerators using Algorithmic Species

In modern embedded systems, heterogeneous architectures are crucial in achieving desired performance requirements under area and energy constraints. Many of these systems combine a multi-processor system-on-chip and a Field Programmable Gate Array to enable hardware acceleration. Although the introduction of High-Level Synthesis significantly reduced the complexity of utilizing these systems, a programmer is still required to have expert knowledge of both the High-Level Synthesis tool and the target hardware and to perform time consuming manual iterations to achieve efficient implementations. In this paper we present SPINE, a design flow for automatic generation of efficient hardware accelerators based on Algorithmic Species. SPINE allows the designer to focus on the algorithm by automatically applying hardware specific optimizations and parallelization techniques to the design. As a case study, we present a design space exploration of nine different loop-nests used in image processing kernels and show how SPINE rapidly generates multiple area-performance trade-offs. Furthermore, we compare our results the state of the art and show that SPINE is a promising direction for accelerator generation as the average performance and area improvement with SPINE are respectively 107% and 75% over the state of the art.