Energy-efficient Static Task Scheduling on VFI based NoC-HMPSoCs for Intelligent Edge Devices in Cyber-Physical Systems

The interlinked processing units in the modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC) based multiprocessor system-on-chip (MPSoC) architecture is becoming a de-facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS therefore, Voltage Frequency Island (VFI) recently adopted for effective energy management mechanism in the large scale multiprocessor chip designs. In this paper, we investigate energy and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI based NoC-MPSoCs with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we propose a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We have introduced a notion of Energy Gradient (EG) that guides EGD in its search for islands voltage settings and minimize the total energy consumption. We conducted the experiments on 8 real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of ~ 24% and ~ 30% over CA-TMES-Search and CA-TMES-Quick respectively.