Design space exploration for co-mapping of periodic and streaming applications in a shared platform

Abstract: As embedded systems advance, the complexity and multifaceted requirements of products have increased significantly. A trend in this domain is the selection of different types of application models and multiprocessors as the platform. However, limited design space exploration techniques often perform one particular model, and combining diverse application models may cause compatibility issues. Additionally, embedded system design inherently involves multiple objectives. Beyond the essential functionalities, other metrics always need to be considered, such as power consumption, resource utilization, cost, safety, etc. The consideration of these diverse metrics results in a vast design space, so effective design space exploration also plays a crucial role. This thesis addresses these challenges by proposing a co-mapping approach for two distinct models: the periodically activated tasks model for real-time applications and the synchronous dataflow model for digital signal processing. Our primary goal is to co-map these two kinds of models onto a multi-core platform and explore trade-offs between the solutions. We choose the number of used resources and throughput of the synchronous dataflow model as our performance metrics for assessment. We adopt a combination method in which periodic tasks are given precedence to ensure their deadlines are met. The remaining processor resources are then allocated to the synchronous dataflow model. Both the execution of periodic tasks and the synchronous dataflow model are managed by a scheduler, which prevents resource contention and optimizes the utilization of available processor resources. To achieve a balance between different metrics, we implement Pareto optimization as a guiding principle in our approach. This thesis uses the IDeSyDe tool, an extension of the ForSyDe group’s current design space exploration tool, following the Design Space Identification methodology. Implementation is based on Scala and Python, running on the Java virtual machine. The experiment results affirm the successful mapping and scheduling of the periodically activated tasks model and the synchronous dataflow model onto the shared multi-processor platform. We find the Pareto-optimal solutions by IDeSyDe, strategically aiming to maximize the throughput of synchronous dataflow while concurrently minimizing resource consumption. This thesis serves as a valuable insight into the application of different models on a shared platform, particularly for developers interested in utilizing IDeSyDe. However, due to time constraints, our test case may not fully encompass the potential scalability of our thesis method. Additional tests can demonstrate the better effectiveness of our approach. For further reference, the code can be checked in the GitHub repository at'.