LAUSR

Aggressive scaling in integrated circuits creates new challenges such as an increase in power density, temperature, and especially process variation in designing Multiprocessor Systems-on-Chip (MPSoC). While most of the previous works attempt to mitigate the process variation effects at the system level, the eventual design still suffers from the variability of frequency and leakage power. In this paper, we propose a method called Stereo that combines stochastic and decomposition to solve task assignment and scheduling under process variation in MPSoCs. In our previous work, we formulated a Mixed Integer Linear Programming (MILP) problem for variation-aware task assignment and scheduling to optimize energy consumption while meeting the real-time constraints. To capture the stochastic behavior of process variation, we employed a chance-constrained programming technique to turn the problem into a corresponding stochastic optimization that can be solved by typical ILP solvers. However, it had a scalability problem. To address this issue, in this work, we leverage a Logic-based Benders Decomposition (LBD) approach to improve the running time for finding an optimal solution of assignments and schedulings under process variation phenomenon). We carried out extensive experiments using Embedded System Synthesis Benchmarks Suite (E3S). The experimental results of the Stereo method evince considerable improvements compared to the baseline method in terms of performance-yield and run-time. The Stereo-based MILP method ameliorates performance-yield up to 2× and run-time by 532×. Moreover, for manifold applications, the Stereo-based LBD method archives 3.47×-91.49× run-time improvement compared to the Stereo-based MILP approach and is capable of assigning and scheduling of more than 50 tasks on 9 processors.