On the Design of Minimal-Cost Pipeline Systems Satisfying Hard/Soft Real-Time Constraints

Pipeline systems provide high throughput for applications by overlapping the executions of tasks. In the architectures with heterogeneity, two basic issues in the design of application-specific pipelines need to be studied: what type of functional unit to execute each task, and where to place buffers. Due to the increasing complexity of applications, pipeline designs face a bundle of problems. One of the most challenging problems is the uncertainty on the execution times, which makes the deterministic techniques inapplicable. In this paper, the execution times are modeled as random variables. Given an application, our objective is to construct the optimal pipeline, such that the total cost of the resultant pipeline can be minimized while satisfying the required timing constraints with the given guaranteed probability. We first prove the NP-hardness of the problem. Then, we present Mixed Integer Linear Programming (MILP) formulations to obtain the optimal solution. Due to the high time complexity of MILP, we devise an efficient $(1+\varepsilon)$ ( 1 + ɛ ) -approximation algorithm, where the value of $\varepsilon$ ɛ is less than 5 percent in practice. Experimental results show that our algorithms can achieve significant reductions in cost over the existing techniques, reaching up to 31.93 percent on average.