(untitled) (INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2022)
The increasing scale and complexity of embedded systems and the use of multi-many-core processors have resulted in a corresponding increase in the demand for software development with a high degree of parallelism. The degree of parallelism in software and the accuracy of performance estimation in the early design stages of model-based development can be improved by estimating performance of blocks in models and utilizing the estimate for parallelization. Research is therefore being performed on a software performance estimation technique that uses the IEEE2804-2019 hardware feature description called software-hardware interface for multi-many-core (SHIM). In SHIM, each LLVM-IR instruction is associated with the execution cycle of the target processor. Because several types of assembly instruction sequences for the target processor are generated from a given LLVM-IR instruction, it is not easy to estimate the number of execution cycles. In this study, we propose a regression analysis method to estimate the execution cycles for each LLVM-IR instruction. It is observed that our method estimated the execution cycles within the target error of ±20% in experiments using a Raspberry Pi3 Model B+.