Across the high-tech systems domain, the need for scalable execution platforms is intensifying. As software becomes more concurrent and performance-sensitive, engineering teams are tasked with maintaining responsiveness and integration fidelity on platforms not originally designed for modern workloads. This is especially acute in semiconductor manufacturing and embedded system environments, where system longevity must coexist with fast-paced software delivery.

The challenge of execution platforms

Modern high-tech systems are increasingly defined by software complexity—characterized by growing concurrency, real-time responsiveness, and modular deployment strategies. Execution platforms, however, are struggling to keep pace. Many rely on distributed architectures combining embedded and server-class hardware, designed for a different era of software needs. The result is a widening gap between platform capabilities and evolving software behavior.

Asynchronous development cycles intensify the challenge

This misalignment is intensified by asynchronous development cycles: hardware refreshes occur every 5 to 10 years, while software evolves continuously. As control loops become more compute-intensive and embedded modelling tasks grow heavier, existing platforms often fall short of performance goals—not due to lack of raw compute power, but due to architectural mismatch. Bottlenecks, reduced system slack, and diagnosability constraints increasingly emerge.

Fit4Future Project

Fit4Future addresses this challenge by investigating execution platform architectures that are scalable, diagnosable, and prepared for next-generation workloads. It builds on trace-based design space exploration methods called DSE 2.0—originally developed under the MASCOT academic program—and extends them with additional perspectives including container-based deployment, startup-time parallelism, and scheduler-aware performance modelling. The aim is to determine how software evolution impacts—and is impacted by—platform design. Can modern workloads make effective use of centralized or hybrid platforms? What architectural trade-offs best preserve system slack and maintainability over time?