Meta-Scheduling Techniques for Energy-Efficient, Robust and Adaptive Time-Triggered Systems Babak Sorkhpour, Prof. Roman Obermaisser, Ayman Murshed Department of Electrical Engineering and Computer Science University of Siegen Germany Kbei, Tehran, Dec 2017

Agenda Introduction Basic Concepts And Related Work MILP & MIQP Meta-Scheduling Techniques for Energy-Efficient, Robust and Adaptive Time- Triggered Systems The inputs models for meta-scheduling Visualizing Scenario-based Meta-Schedules for Adaptive Time-Triggered Systems Example Scenario And Results Outputs and results Conclusion

Introduction This work is motivated to use of dynamic voltage frequency scaling (DVFS) for both communication and computation sides to solve the energy-efficient problem in the whole of NoC structure in MPSoCs. In this work, an algorithm is developed, which supports the mapping and scheduling of jobs to NoC architectures for minimizing the total energy consumption considering the time constraints and adjustable frequencies . This algorithm is intended for mixed-criticality, and safety-critical adaptive TT systems and can cover fault-tolerance requirements.

Basic Concepts And Related Work Time-Triggered Embedded Systems Worst Case Execution Time Power-Energy Saving Technique on-chip routers and links consume up to 18% in the Intel SCC, 20% in the Alpha 21364 processor, communication power takes 33% in RAW architectures [5]. [9] reported that 28%∼36% of the total chip power consumption depends on NoC energy (e.g., on-chip switches and links) consumption

MILP & MIQP If the problem contains an objective function with no quadratic term, (a linear objective), then the problem is termed a Mixed Integer Linear Program(MILP)*. However, if there is a quadratic term in the objective function, the problem is termed a Mixed Integer Quadratic Program (MIQP)*. In our work, MIQP problem is using to find a global optimum solution and so the MILP & MIQP problems are solved using the IBM CPLEX optimizer. * IBM® Knowledge Center

Power Models The total execution time (ExecTimej) and energy consumption (Energy) of a job j on a CMOS-based processor can be estimated by: ExecTime j ≈ 1 𝑓 and Power ≈ C ∙ 𝑓∙ V2 Energy= Power ∙ ExecTime Since in DVFS systems 𝑉 varies approximately with 𝑓 (𝑉 ∝ 𝑓), the performance-energy trade-off of frequency scaling can be expressed as ExecTime j ∝ 1 𝑓 and Energy ∝ 𝑓 2 [25]. 𝑉 ∝ 𝑓 →Power ≈ 𝑓3 → Energy= 𝑓3 ∙ExecTime j

The inputs models for meta-scheduling AM with constants of application (e.g., number of jobs, WCET of each job, precedence constraints). PM with constants of the platform (e.g., number of nodes, links between nodes). SM with values for decision variables (e.g., allocation to node, start time of execution). CM with fault and events details(e.g., event type, event execution time).

Static-Scheduling Collision Avoidance Constraint Connectivity Constraints Job Allocation Hop Count Path and Visited Cores

Meta-Scheduling Techniques for Energy-Efficient, Robust and Adaptive Time-Triggered Systems This technique is trying to solve scheduling problems in scenario-based scheduling and quasi-static task mapping which in some papers is called super-scheduling. In this work, a Meta-Scheduler (MES) tool to solve meta-scheduling problems developed. We propose an energy-efficient scheduling algorithm regarding dynamic-slack and slowdown factors in multi-scenario-based systems for NoC-based MPSoC.

Scheduling Decision Variables Slow Down Factors: SlowDF= j1 ⋮ ⋮ jm ∈ 𝑀𝑖𝑛,…,𝑀𝑎𝑥 ∧𝑀𝑖𝑛>0 Scheduling Constraints Job Dependency Constraints : INJECTIME[m1] + EXECTIME[j1] ∙SLOWDF[j1]+ ( ( HOPS[m1]+1 ) ∙DUR[m1] ) <= INJECTIME[m2] EXECTIME[j1]*SLOWDF[j1] <= INJECTIME[m2] Message Duration : 𝐷𝑢= du1 ⋮ ⋮ dum ∈ {1,…,𝑀𝑎𝑥} 𝑚 Message Deadlines : ∀j1 ∈ 1,…,𝑗 , ∀m1 ∈ 1,…,𝑚 ,∀ℎm1 ∈ {1,…,𝑀𝑎𝑥H } : 𝑖m1+ ℎm1+1 ∙𝐷𝑢m1≤𝐷j1

𝐶𝑃 job1 =(ExecTime j1∙(SlowDF j1) 2 ) Objective Function The objective is to maximize the energy-efficiency. In other words, it is minimizing energy consumption by increasing job execution times regarding.   ∀ job1 ∈ {1,…,j } : 𝐶𝑃 job1 =(ExecTime j1∙(SlowDF j1) 2 ) 𝑚𝑎𝑥𝑖𝑚𝑖𝑧𝑒 ( j=1 𝑗 𝐶𝑃 )

Visualization of schedules Meta-Scheduling Visualizer (MeSViz) is an advanced and applicative tool which is designed and planned to visualize and display SM contents (e.g., jobs, cores, messages, allocation, assignment. For calculating the energy consumption of static-slack SM (EnergySM), an average of all dynamic-slack SM’s (EAvgSMdynamic) and compare the results (SavingE) MeSViz is used follows the formula: EnergySM= ETji SlowDFji 2 , EAvgSMdynamic= 𝑛=1 𝑛 ESMn 𝑛 SavingE=( ESMstatic − EAvgSMdynamic ESMstatic ).100

Example Scenario And Results Input Model Input Name ID Data AM Job WCET=2 1 WCET=4 2 WCET=6 3 WCET=8 4 WCET=10 Message Quantity=6 ID start=0 Deadline All job= 1185 Slowdf All job: min =1 & max =100 SlackEvent All job= 50% PM Hop (Switch) Quantity=2 ID start=0 Core Quantity=5 ID start=6 Link

Results Static 1.875 Dynamic 1.62 13.6% 0.6675 64.4% 1.0948 41.61% SM ID Slack Mode Energy Consumption Energy Saving Static 1.875 49 Dynamic 1.62 13.6% 5 0.6675 64.4% Average 1.0948 41.61% SM ID- Note Slack Mode Job ID ET SLOWDF Static 2 4 1 6 3 8 10 49 (Minimum Energy saving) Dynamic 5 5 (Maximum Energy Saving)

Conclusion The simulation results show that our dynamic-slack algorithm, compared to the static-slack, produces a maximum of 64.4% in a single schedule and 41.61% energy-saving of NoCs on average: A novel MeS technique is used to reduce the dynamic power consumption in the schedules. MeS can be used in scenario-based (fault, safety, power-saving) scheduling and adaptivity TT systems. further work still needs to be done in the future (e.g., energy-saving on communication links, fault-injection and more scenarios related to safety). ACKNOWLEDGMENT The Europian project H2020 SAFEPOWER has supported this work under the Grant Agreement No. 687902. http://safepower-project.eu/

Thank you for your attention. Babak. Sorkhpour@uni-siegen Thank you for your attention. Babak.Sorkhpour@uni-siegen.de http://networked-embedded.de