Directed Acyclic Graphs (DAGs) are widely deployed as task models in autonomous systems, including vehicles and drones, to capture functional dependency. DAG scheduling has been extensively investigated by various communities to shorten makespan, under the common assumption that the model itself is given a priori. This work studies a rarely touched problem - construction of DAG models - and considers time-triggered blended task chains predominant in autonomous systems. We report representation semantics and a topology optimization method. Experiments show that the average end-to-end response time reduction is 4.8 times of the conventional Floyd algorithm. Our time complexity is $\mathcal{O}\left(n^{2}\right)$, making it suitable for handling dynamic tasks as well.

Chiplets promise customized design for different sets of applications with heterogeneity, where there are various kinds of resources to manage. Examples are GPUs, NPUs, and CPUs for computation of AI and general tasks, memory, communication, as well as logical resources such as I/Os. There can be strong contention in access of resources across chiplets by applications, which sometimes have discrepant requirements. Resolving such contention is critical to fully exploit the resources on heterogeneous chiplets and satisfy the needs of dynamic application workloads. This talk will discuss a set of methods that manage these resources during the design phase as well as at runtime, which should interact with the architecture exploration and contribute to the entire chiplets systems design altogether.