As the computing landscape evolves, system designers continue to explore design methodologies that leverage increased levels of heterogeneity to push performance within limited size, weight, power, and cost budgets. One such methodology is to build Domain-Specific System on Chips (DSSoCs) that promise increased productivity through narrowed scope of their target application domain. In previous works, we have proposed CEDR, an open source, unified compilation and runtime framework for DSSoC architectures that allows applications, scheduling heuristics, and accelerators to be co-designed in a cohesive manner that maximizes system performance. In this work, we present changes to the application development workflow that enable a more productive and expressive API-based programming methodology. These changes allow for more rapid integration of new applications without sacrificing application performance. Towards the design of heterogeneous SoCs with rich set of accelerators, in this study we experimentally study the impact of increase in workload complexity and growth in the pool of compute resources on execution time of dynamically arriving workloads composed of real-life applications executed over architectures emulated on Xilinx ZCU102 MPSoC and Nvidia Jetson AGX Xavier. We expand CEDR into the application domain of autonomous vehicles, and we find that API-based CEDR achieves a runtime overhead reduction of 19.5% with respect to the original CEDR.