Mercury: Instruction Pipeline Aware Code Generation for Simulink Models

Simulink is a widely used model-driven design environment for supporting the simulation and code generation of embedded applications. To improve the quality of the code generated from Simulink models, state-of-the-art code generators employ various high-level optimizations, like eliminating local variables. However, they overlook the compatibility between code and low-level processor architecture, especially the instruction pipeline. Consequently, instruction pipeline stalls occur frequently, leading to additional delays in instruction execution, as well as limited efficiency for deployed embedded software. In this paper, we propose, an instruction pipeline aware code generator for Simulink models which utilizes data dependencies between actors to decrease the instruction pipeline stalls of the generated code. First, collects data dependencies through model dataflow traversal and records the property of each actor. Then, approximately estimates the execution latency of required instructions fetched from corresponding actors and uses a topology-based method to obtain candidate actors for code synthesis. Finally, adopts the least penalty priority to iteratively select the most suitable actor for code synthesis and releases data dependencies with its subsequent actors. We implemented and evaluated on benchmark Simulink models su2021code as well as a real industrial model. Compared to the official tool Simulink Embedded Coder and the state-of-the-art academic tool DFSynth, outperformed them by and in terms of the execution time of the generated code across different architectures, respectively. The statistics also demonstrate that the generated code of increases utilization of pipeline slots by and, respectively.