To adapt the EtoE-DSE methodology for exploring the design space of heterogeneous computing platforms, several strategies can be employed. First, the framework should be extended to support multiple types of processing units, including CPUs, GPUs, and specialized accelerators like FPGAs or ASICs. This can be achieved by developing a unified abstraction layer that allows the EtoE-DSE to interact with different hardware architectures seamlessly.
The latency estimation model must also be modified to account for the unique characteristics of each processing unit. For instance, GPUs may have different latency profiles due to their parallel processing capabilities, while FPGAs may offer lower latency for specific tasks but require additional configuration time. The EtoE-DSE approach should incorporate these factors into its latency estimation algorithms to provide accurate predictions for heterogeneous systems.
Moreover, the design space segmentation process can be enhanced to consider the resource availability and performance characteristics of each processing unit. This involves creating a mapping strategy that optimally assigns tasks to the most suitable processing unit based on their computational requirements and the current system state. The frequency-based design space segmentation (FDSS) process can be adapted to include considerations for the different operating frequencies and power profiles of each type of processor.
Finally, the optimization algorithms used in the LCSO process should be tailored to handle the multi-objective nature of heterogeneous systems, where trade-offs between energy consumption, performance, and area may differ significantly across different processing units. By incorporating these adaptations, the EtoE-DSE methodology can effectively explore the design space of heterogeneous computing platforms, ensuring that the resulting configurations meet the stringent requirements of modern embedded systems.