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.

翻译：摘要：随着计算格局的演进，系统设计者持续探索利用更高异构性来提升有限体积、重量、功耗及成本预算内性能的设计方法。其中一种方法是构建领域专用片上系统（DSSoC），通过缩小目标应用领域的范围来提升开发效率。在先前工作中，我们提出了CEDR——一个面向DSSoC架构的开源统一编译与运行时框架，能够以最大化系统性能的协同方式实现应用程序、调度启发式算法与加速器的联合设计。本研究提出对应用程序开发流程的改进，通过基于API的编程方法论实现更高效率与表现力。这些改进在不牺牲应用性能的前提下，支持更快速地集成新应用。为设计具有丰富加速器集合的异构SoC，本实验研究了在基于Xilinx ZCU102 MPSoC与Nvidia Jetson AGX Xavier仿真架构上执行的真实应用构成的动态到达工作负载中，工作负载复杂度提升与计算资源池增长对执行时间的影响。我们将CEDR扩展至自动驾驶应用领域，发现基于API的CEDR相较原始CEDR实现了19.5%的运行时开销降低。