1 / 35

Energy-Centric Scheduling for Real-Time Systems

This publication discusses the need for low power and power-aware design in real-time systems. It explores the concept of ambient intelligence and its applications in electronic devices such as wearable assistants and smart pills. The paper also addresses the challenges and solutions in designing energy-efficient architectures and optimizing power consumption through techniques like dynamic power management and dynamic voltage scaling.

melodyp
Download Presentation

Energy-Centric Scheduling for Real-Time Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Energy-Centric Scheduling for Real-Time Systems Prof. Jan Madsen Informatics and Mathematical Modelling Technical University of Denmark Richard Petersens Plads, Building 321 DK2800 Lyngby, Denmark

  2. Outline • The need for low power • Design of real-time systems • Power-aware design (c) Jan Madsen

  3. Towards Ambient Intelligence [Weiser] • Wireless network delivers infotainment, communication, navigation, ... anyplace, anytime, for every citizen ... • Hidden, pervasive computing. IT to background, people in the foreground, improves quality of life in non-invasive way ... • Things see, listen, feel, becomes sensitive and adaptive to people ... (c) Jan Madsen

  4. Electronic Devices Support Athletes ECG, Blood Pressure Blood Composition (e.g. lactate) Wearable Digital Assistant Wireless Link to Coach and Med Team Multiple Hop BAN Position & Force Sensors curtsies Rudy Lauwereins (MPSOC02) (c) Jan Madsen

  5. Smartshirt - wearable computing (c) Jan Madsen

  6. ... or implants (c) Jan Madsen

  7. Electronic devices for diagnostics (c) Jan Madsen

  8. Smart pills – 1st generation (c) Jan Madsen

  9. Smart pills – 2nd generation (c) Jan Madsen

  10. SoC Wearable Assistants RF See Hear Feel Speak Show Stimulate IF IF 1/person Global System for Ambient Intelligence • Multimedia, games • QoS • GPS • Global connectivity • Biometric input • Health ... • Ambient control 10 ... 100 Gops 0.1-2W (c) Jan Madsen

  11. 1000 m GSM/UMTS basestations 10 m Ad hoc network 1 m RF RF T T C C Ambient transducers BAN body transducers Global System for Ambient Intelligence • SoC • Wearable Assistants • Multimedia, games • QoS • GPS • Global connectivity • Biometric input • Health ... • Ambient control RF See Hear Feel Speak Show Stimulate IF IF 10 ... 100 Gops 0.1-2W >100/person aura after Rudy Lauwereins (MPSOC02) (c) Jan Madsen

  12. Transducer node Ultra low energy (100Mops/mW) Low flexibility Ultra low cost (1$) 1..10 Mtr (small size) Low clock frequency DSP and RF dominated Chip package codesign Ultra fast hardware design Assistant node Low energy (10..50 Mops/mW) High flexibility Low cost (100$) 10..100 Gops, >100 Mtr High clock frequency Data-intensive, dynamic tasks Task and data concurrency Incremental software design What are the properties of these Ambient Intelligence architectures ”PLATFORM” ”PACKAGE in a week” @ 100..1000 times power efficiency of today’s μP (c) Jan Madsen

  13. Design challenge min Design cycle (c) Jan Madsen

  14. 3 2 1 4 1 2 3 4 os mapping a b c c a b Design of real-time systems (c) Jan Madsen

  15. 1 2 Break processes to increase parallelism 3 Cluster processes to reduce communication deadline b a a 2 & 3 1 b Principles of mapping Partitioning/clustering Allocation Mapping Scheduling Communication 1 3 2 (c) Jan Madsen

  16. Power consumption • PCMOS = Pstatic + Pdynamic • Pdynamic ~ a f C Vdd2 • Power minimization, lower: • switching activity • clock frequency • capacitive load • supply voltage (c) Jan Madsen

  17. V1 V2 a Power reduction • Dynamic power management (DPM) • Based on processor power modes • Intel 80200 • Dynamic voltage scaling (DVS) • Frequency and supply voltage can be adjusted at run-time • Usually these are discrete values and not continuous (c) Jan Madsen

  18. r1 r1 r1 r1 idle r1 idle r1 idle 1 1 2 2 3 3 Power reduction: DPM r1 r1 r1 (c) Jan Madsen

  19. 1 2 3 V1 V2 a a Power reduction: DVS Power profile 1 2 3 a mem 1 2 3 (c) Jan Madsen

  20. t1 2 Mapping f(t,p) 1 3 p1 time p2 p3 Optimizing a single task Exploring the design space (c) Jan Madsen

  21. t1 t3 t2 3 2 1 p1 Optimizing a single task Exploring the design space f(t,pe) Mapping time (c) Jan Madsen

  22. t1 V1 V2 Mapping f(t,p) V1 V2 p1 time Optimizing a single task using DVS Exploring the design space (c) Jan Madsen

  23. t1 V1 V2 Mapping f(t,p) p1 time Optimizing a single task using DVS (c) Jan Madsen

  24. t2 t3 p2 2 1 p1 Mapping 3 p1 p2 Optimizing three tasks t1 (c) Jan Madsen

  25. t2 t3 1 com p1 p2 Optimizing three tasks t1 p2 2 p1 Mapping 3 (c) Jan Madsen

  26. t2 t3 1 com p1 p2 Optimizing three tasks t1 p2 2 p1 Mapping 3 (c) Jan Madsen

  27. t2 t3 2 1 2 com p1 p2 Optimizing three tasks t1 p2 p1 Mapping 3 (c) Jan Madsen

  28. Contributions by Flavius Gruian • Task level scheduling • Power optimization of a single task • Task group scheduling • With and without dependencies • Uni- and multi-processor systems • Architecture selection and scheduling • Considering task assignment as part of the optimization (c) Jan Madsen

  29. Task level scheduling (c) Jan Madsen

  30. Task group scheduling • Tasks with dependencies (task graph) • Static scheduling on uni- and multi-processor systems • List scheduling with energy-sensitive priority function • Tasks without dependencies (task set) • Dynamic scheduling on uni-processor systems • Minimum required task speed of RM and EDF scheduling • Extending EDF to include an ordering policy for achiving low power at run-time (c) Jan Madsen

  31. 2 3 1 1 2 4 Task graph scheduling (c) Jan Madsen

  32. Task set scheduling • Maximum required speed • Method to achieve the smallest possible processing speeds for which the task set is still schedulable. • This part is done off-line • Applied to both EDF and RM scheduling • Slack distribution • An early finishing task may pass unused processor time to the next tasks • Applied to RM scheduling (c) Jan Madsen

  33. RM with slack distribution Post-execution analysis, tasks stretch to max processor utilization As All, but assuming exact knowledge of task exec pattern Run-time speed scheduling using slack distribution and stochastic scheduling, plus off-line and strech Off-line RM-MRS followed bysimple run-time speed schedule (c) Jan Madsen

  34. Task set scheduling • Uncertainty-based scheduling • Probabilistic execution times of tasks • Off-line task sequencing to improve run-time decisions • Run-time speed scheduler which adjust processor speed when ever a task finishes • UBS for EDF (c) Jan Madsen

  35. Architecture selection and scheduling • Solving the combined task assignment and scheduling problem • Solution for fixed-speed and variable-speed processors (c) Jan Madsen

More Related