Knight: Optimizing Code Generation for Simulink Models With Loop Reshaping

Simulink has become a pivotal infrastructure in embedded scenarios, including automotive systems and aerospace designs. To improve the performance of the code generated from Simulink models, state-of-the-art code generators employ various optimization techniques, such as expression folding, variable reuse, and parallelism. However, they struggle to generate efficient code for loop-semantic models which are crucial in substantial data processing tasks. This inefficiency manifests in numerous redundant calculations, such as array calculations and conditional statements. As a result, the performance of the generated code is limited. This article proposes Knight, an efficient code generator for loop-semantic Simulink models with loop reshaping. Knight first parses the Simulink model to extract essential content, such as block functionalities and connections. Knight then identifies blocks with internal states and implements the specific interaction rules to discern those that are state-dependent. For state-dependent blocks, Knight conducts forward inference to obtain their preceding blocks, which influence the internal state calculations. Subsequently, Knight isolates blocks that are optimizable and irrelevant to the internal states. These blocks are strategically relocated outside the loop semantics while preserving critical semantics related to code generation. We implemented and evaluated Knight on benchmark Simulink models across different compilers and architectures. Compared with the state-of-the-art code generators Simulink Embedded Coder, DFSynth, and HCG, the code generated by Knight is $16.58 \times $ faster, $16.89 \times $ faster, and $15.38 \times $ faster in terms of execution duration on average, without incurring additional overhead of memory usage.