When applied to systems with highly dynamic and unpredictable timing behavior, the EtoE-DSE approach faces several challenges and limitations. One significant challenge is the accurate estimation of end-to-end latency. In systems where timing can vary due to factors such as workload fluctuations, environmental changes, or resource contention, the static models used for latency estimation may become inadequate. This can lead to suboptimal design choices that do not meet the real-time requirements of the system.
Another limitation is the scalability of the design space exploration process. As the complexity of the system increases, particularly in dynamic environments, the number of potential configurations can grow exponentially. This makes exhaustive exploration infeasible and can hinder the effectiveness of heuristic or genetic algorithms used in the DSE process. The dynamic nature of the system may also require real-time adjustments to the design space exploration strategy, which could complicate the implementation of the EtoE-DSE methodology.
Additionally, the interaction between components in a dynamic system can introduce unforeseen dependencies and timing constraints that are not captured in the initial design models. This necessitates a more adaptive approach to DSE, where the methodology can respond to changes in system behavior in real-time, potentially requiring the integration of machine learning techniques to predict and adapt to these changes.