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Jiwon Hahn ECE295 Seminar December 2, 2002

I ntegrated M anagement of P ower A ware C omputing & C ommunication T echnologies. Jiwon Hahn ECE295 Seminar December 2, 2002. IMPACCT Project. People Faculty: Pai Chou, Nader Bagherzadeh Students: Jinfeng Liu, Dexin Li, Bita Gorji-Ara, Duan Tran, and Jiwon Hahn

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Jiwon Hahn ECE295 Seminar December 2, 2002

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  1. Integrated Managementof Power Aware Computing& Communication Technologies Jiwon Hahn ECE295 Seminar December 2, 2002

  2. IMPACCT Project • People • Faculty: • Pai Chou, Nader Bagherzadeh • Students: • Jinfeng Liu, Dexin Li, Bita Gorji-Ara, Duan Tran, and Jiwon Hahn • Collaborator • NASA JPL, Rockwell Collins, ISI • Sponsors • DARPA PAC/C, Broadcom, HP

  3. Outline • What is IMPACCT? • Motivation • How it works • Tools • Experiments and Results • Conclusions and Future Works

  4. What is IMPACCT? • A CAD tool for exploring power/performance tradeoffs • A new technique that performs component, system, and mission-level integrated power management • Target applications • Mars pathfinder, ATR, UCAV,…

  5. Motivation • Embedded Systems • Computers inside devices • PDA, cellphone, camera, vehicles, robots,… • Power management • Power-Aware vs. Low-power • Mission-Aware: meet the constraints • High-level approach • Amdahl’s law: Power saving of a component must be scaled by its percentage contribution to entire system • Evaluate combined effects in the context of system • Higher abstraction level enables global optimum

  6. How it works • Hierarchical power management • System-level power scheduling • Power-aware scheduling • Mode selection • Mission-level power scheduling • Schedule Selection

  7. Scheduling & Mode Selection • Power Aware Scheduling • Schedules tasks, meeting timing and power constraints • Output: initial schedule • Static scheduling/planning [DAC'01] • Mode Selection • Selects resource modes of each task considering mode dependency • Minimize energy consumption • Output: mode schedule • Mode selection/modeling [ASPDAC’02]Winner Best Student Paper Award

  8. Schedule Selection • Goal • To generate a mission level schedule which • Adapts to variable power constraint • Considers higher-level context change overhead • Meets the global deadline • Assumptions • A mission contains one or more applications • It is static

  9. Previous Schedule Overhead Matrix Current schedule Schedule Set P … Put N schedules here! 0 1 2 … M D Schedule Selection • Problem: • Select N schedules • among M different schedules, • by deadline D, • under the maximum power curve P. • Minimize energy • considering overhead

  10. Schedule Selection(cont.) • Parameters: t,n,k,m • t: timestamp. (discrete value) 0tD • n: schedule count excluding S0 1nN • k: schedule count including S0. (for tracking the selected path) 1kD • m:current schedule 0mM

  11. n n t t =1 k m m t =2 t = D Schedule Selection(cont.) • Algorithm: 4D Dynamic Programming • Idea: • Reach the global optimum by keeping track of optimal solutions of subproblems • Optimal substructure • For some(k,m), min{E(t,n,m)} contains the optimal value. • Space • D3 for keeping optimal Energy • D4 for bookkeeping indices • Speed • O(D3) – polynomial! • Could be optimized for speed-up Bookkeeping Cubes Energy Cube

  12. Schedule Selection(cont.) • Notation • Set of schedule, S= {S0, S1, … ,SM} • Time period of each schedule: Ts[0…M] • Power level of each schedule: Ps[0…M] • Energy of each schedule: Ts x Ps = Es[0…M] • Schedule-switch overheads from Si to Sj: Po(i,j), To(i,j), Eo(i,j)

  13. Schedule Selection(cont.) • Algorithm • Initialization • If (t,n,k,m) is the first possible selection, • E(t,n,k,m) = directly calculated energy • Else • E(t,n,k,m) =  • Process • if m!=0, • E(t,n,k,m)=E(t’,n-1,k-1,m’)+Eo(m’,m)+Ps(m)Ts(m) • t = t’+To(m’,m)+Ts(m) • if m=0, • E(t,n,k,m)= E(t’,n,k-1,m’)+Eo(m’,m)+Ps(m)Ts(m)

  14. Mission-level Constraints: Power and Deadline • Mission Schedule System-level  Mission-Level • Input: • Application Model • ports, channels,… • Architecture Model • System architecture template • Component library + mode dependency model(MDM) • Constraints • Power and Timing • Output: • Mode Schedule

  15. Mission-Level2 Mission Constraints Scheduler Initial Schedule Mode Selector Overhead Calculator MS MS MS MS MS … Schedule Collector Schedule Selector 2 MissionSchedule System-level1 Sys. Arch. Template App. Model Component Library + MDM Constraints 1 1) Mode Schedule

  16.  Mission-Level Mission Power Constraint Curve • Mission Schedule: Mission Deadline System-level • Initial Schedule: • Mode Schedule:

  17. Tools • Scheduler (Jinfeng) • Mode Selector (Dexin) • Schedule Selector (Jiwon) • Etc.. • Programs and tutorial are available

  18. Tools(I): Scheduler

  19. Tools(II): Mode Selector

  20. Tools(III): Schedule Selector

  21. Experiments and Results I • Mars Rover • Comparison over 3 scenarios • Overall mission • 3 power scenarios: best, typical, worst, 10 min each • 48 steps • Power-aware schedules • Accelerated speed by tracking available power • Finished earlier before working in the worst case • 33% faster, 32.7% less energy cost

  22. Experiments and Results I(cont.)

  23. Experiments and Results II • Mars Rover • Behaviors and tasks • Moving around on Mars surface • Communicating with the Lander • Taking pictures • Performing scientific experiments • Components in the entire system • Hazard detector, Driving motor, Steer motor, Radio frequency modem (RF), Camera (CAM), Microprocessor (PPC), Micro-controller

  24. Experiments and Results II(cont.) • On/off only • Relaxed constraints • Mode change overhead • No max power constraint • Mode selection • Energy saving:From 6.9% to 49.3% average 26.5% • Meets max power

  25. 3 3-1 0-3 1 3s, 15J, 45W 5s, 10J, 50W 10s, 5J, 50W Experiments and Results III • Example of Schedule Selection • Schedule Set: 0 1 2 3 • Overhead Matrix 20s

  26. Low Power 1 1 20s • Greedy 3 3-1 20s 0-3 1 • Dynamic Programming 2 0-2 2-1 1 20s Experiments and Results III(cont.) • Exceed deadline • Energy = 149W • Nonoptimal solution • Energy = 131W • Optimal solution!

  27. Experiments and Results III(cont.)

  28. Conclusions • IMPACCT • greatly expands the range of power/performance trade-offs • effectively integrates existing power management techniques • models system-level dependencies • saves great amount of energy consumption while meeting all constraints • proposes novel hierarchical power management technique

  29. Current & Future Works • Ongoing work • Architecture Modeling(Dexin) • Mission-level Power Management(Jiwon) • Extended experiments on broadcom and itsy board(Jinfeng) • Applying to different application: SDR(Bita) • Future work • Dynamic Power Management • Mixed application schedule selection • More applications

  30. Reference • Pai H. Chou, Jinfeng Liu, Dexin Li, and Nader Bagherzadeh, “IMPACCT: Methodology and Tools for Power-Aware Embedded Systems”, Design Automation for Embedded Systems • J. Liu, P. Chou, N. Bagherzadeh, and F. Kurdahi. “Power-aware scheduling under timing constraints for mission-critical embedded systems”. In Proc. 38th Design Automation Conference, pages 840–845, June 2001 • D. Li, P. Chou, and N. Bagherzadeh. Mode selection and mode-dependency modeling for power-aware embedded systems. In Proc. 7th Asia South Pacific Design Automation Conference, pages 697–704, January 2002 • J. Hahn, P. Chou, and N. Bagherzadeh, “Tutorial: IMPACCT Tool v1.0”, University of California at Irvine, August, 2002 THANK YOU!

  31. THANK YOU!

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