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Adaptive Video Coding to Reduce Energy on General Purpose Processors

Adaptive Video Coding to Reduce Energy on General Purpose Processors. Daniel Grobe Sachs, Sarita Adve, Douglas L. Jones University of Illinois at Urbana-Champaign http://www.cs.uiuc.edu/grace grace@cs.uiuc.edu. Introduction. Wireless multimedia increasingly common

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Adaptive Video Coding to Reduce Energy on General Purpose Processors

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  1. Adaptive Video Coding to Reduce Energy on General Purpose Processors Daniel Grobe Sachs, Sarita Adve, Douglas L. Jones University of Illinois at Urbana-Champaign http://www.cs.uiuc.edu/grace grace@cs.uiuc.edu

  2. Introduction • Wireless multimedia increasingly common • Recent advances reduce constraints: • 2GHz+ processors • High-speed wireless networks • Systems now Energy limited • Energy management essential

  3. Adaptation • Adaptation key to energy management • Hardware adaptation already common • Software adaptation also possible • Challenges • How do we control adaptations? • How do we coordinate different adaptations?

  4. GRACE Project • Target mobile multimedia devices. • Coordinated adaptation of all system layers • Hardware, application, network, OS • Complete cross-layer adaptation framework • Preserves separation between layers

  5. Goals of this work • Target wireless video transmission • Adapt application: Adaptive video encoder • Adapt hardware: Adaptive CPU • Implement part of GRACE framework • Trade off between CPU and network energy

  6. Contributions • Apply existing adaptive-CPU research • Energy-adaptive video encoder • Trades off between network, CPU • Allows adaptation with fixed QoS • Cross-layer adaptation framework • Coordinate app and CPU adaptation • Preserves logical separation between layers • 20% Energy savings over existing systems

  7. Presentation Overview • System model • System architecture and design • Cross-layer adaptation process • Results

  8. System Model Adaptive CPU • Total Energy = CPU Energy + Network Energy Adaptive Video Encoder Wireless Network Video Capture Control

  9. CPU Hardware Adaptation [Micro] • Reduce performance to save energy • Voltage and frequency scaling • Lower freq  lower voltage  lower energy • Architecture adaptation • Issue width • Active functional units (ALUs, etc.) • Instruction window size

  10. Adaptive Encoder • Based on TMN H.263 encoder • Changed to logarithmic motion search • Encoder adapts for energy • Trade off between network and CPU energy • More computation  fewer bits • Adapt Motion Search and DCT • Computationally expensive • Elimination affects primarily rate

  11. Adaptive Encoder Details • Motion Search and DCT thresholds • Terminate MS early when SAD under threshold • Skip DCT if SAD of block under threshold • Transmit “DCT flag” bit for each 8x8 block • Extends H.263 standard • Adaptation effect: • Setting thresholds at infinity • Reduces CPU load by ~50% • Increases data rate by 2x or more

  12. Adaptation Control • When do we adapt? • What configurations do we choose?

  13. Adaptation Control • When do we adapt? • Adapt before every frame • What configurations do we choose?

  14. Adaptation Control • When do we adapt? • Adapt before every frame • What configurations do we choose? • Must minimize total CPU+network energy • Must complete frame within its allocated time

  15. Adaptation Control • When do we adapt? • Adapt before every frame • What configurations do we choose? • Must minimize total CPU+network energy • Must complete frame within its allocated time • How do we find the optimal configurations?

  16. Optimization • Application, CPU reconfiguration linked • Application reconfiguration changes workload • CPU reconfiguration changes performance • App config affects optimal CPU configuration … and vice versa • Two stage approach 1. For each app config, find CPU config, energy 2. Pick lowest-energy application configuration

  17. Optimization Algorithm 1. For each app config, find • Best CPU config • CPU energy • Network energy • Total energy = CPU energy + network energy 2. Pick app config with lowest total energy

  18. Optimization Algorithm 1. For each app config, find • Best CPU config • completes in time, with least energy [MICRO’01] • CPU energy • Network energy • Total energy = CPU energy + network energy 2. Pick app config with lowest total energy

  19. Optimization Algorithm 1. For each app config, find • Best CPU config • completes in time, with least energy [MICRO’01] • CPU energy • Network energy • Total energy = CPU energy + network energy 2. Pick app config with lowest total energy Requires instruction count

  20. Optimization Algorithm 1. For each app config, find • Best CPU config • completes in time, with least energy [MICRO’01] • CPU energy = Instruction count x Energy per instruction [MICRO’01] • Network energy • Total energy = CPU energy + network energy 2. Pick app config with lowest total energy Requires instruction count

  21. Optimization Algorithm 1. For each app config, find • Best CPU config • completes in time, with least energy [MICRO’01] • CPU energy = Instruction count x Energy per instruction [MICRO’01] • Network energy = Byte count x Energy per byte [WaveLAN measured] • Total energy = CPU energy + network energy 2. Pick app config with lowest total energy Requires instruction count

  22. Optimization Algorithm 1. For each app config, find • Best CPU config • completes in time, with least energy [MICRO’01] • CPU energy = Instruction count x Energy per instruction [MICRO’01] • Network energy = Byte count x Energy per byte [WaveLAN measured] • Total energy = CPU energy + network energy 2. Pick app config with lowest total energy Requires instruction count Requires byte count

  23. Adaptation Process: Stage 1 CPU Net Predict Next Instr. Count Predict Next Byte. Count App. Conf. 1 Conf 1 Energy Conf 2 Energy Conf 3 Energy Conf n Energy . . . App configuration energy table

  24. Adaptation Process: Stage 1 CPU Net Predict Next Instr. Count Predict Next Byte. Count App. Conf. 1 CPU Optimizer Find CPU Configuration Conf 1 Energy Conf 2 Energy Conf 3 Energy Conf n Energy . . . App configuration energy table

  25. Adaptation Process: Stage 1 CPU Net Predict Next Instr. Count Predict Next Byte. Count App. Conf. 1 CPU Optimizer Find CPU Configuration CPU Energy Estimator Predict CPU Energy Predict Net Energy Network Energy Estimator Conf 1 Energy Conf 2 Energy Conf 3 Energy Conf n Energy . . . App configuration energy table

  26. Adaptation Process: Stage 1 CPU Net Predict Next Instr. Count Predict Next Byte. Count App. Conf. 1 CPU Optimizer Find CPU Configuration CPU Energy Estimator Predict CPU Energy Predict Net Energy Network Energy Estimator + Conf 1 Energy Conf 2 Energy Conf 3 Energy Conf n Energy . . . App configuration energy table

  27. Adaptation Process: Stage 1 CPU Net Predict Next Instr. Count Predict Next Byte. Count App. Conf. 1 CPU Optimizer Find CPU Configuration CPU Energy Estimator Predict CPU Energy Predict Net Energy Network Energy Estimator + Conf 1 Energy Conf 2 Energy Conf 3 Energy Conf n Energy . . .

  28. Adaptation Process: Stage 1 CPU Net Predict Next Instr. Count Predict Next Byte. Count App. Conf. 1 CPU Optimizer Find CPU Configuration CPU Energy Estimator Predict CPU Energy Predict Net Energy Network Energy Estimator + Conf 1 Energy Conf 2 Energy Conf 3 Energy Conf n Energy . . .

  29. Adaptation Process: Stage 2 Conf 1 Energy Conf 2 Energy Conf 3 Energy Conf n Energy . . .

  30. Adaptation Process: Stage 2 Conf 1 Energy Conf 2 Energy Conf 3 Energy Conf n Energy . . . Pick Lowest Energy

  31. Adaptation Process: Stage 2 Conf 1 Energy Conf 2 Energy Conf 3 Energy Conf n Energy . . . Pick Lowest Energy Chosen Configuration CPU Adaptor Application Adaptor

  32. Adaptation Process: Stage 2 Conf 1 Energy Conf 2 Energy Conf 3 Energy Conf n Energy . . . Pick Lowest Energy Chosen Configuration CPU Adaptor Application Adaptor Capture, Encode, and Transmit Frame

  33. Predictors • How do we predict instructions and bytes? • Fixed software  use previous frame data • Adaptive software  no longer works! • Solution: Offline profiling • Encode reference sequences offline • Transition randomly between app. configs • Fit predictors to transitions between configs • Map last instruction, bytes to new app. config • Linear, 1st-order predictors

  34. Experiments • RSIM CPU simulator • State-of-the-art CPU, memory • Princeton Wattch energy model • Reported energy typical of modern CPUs • Simulation Conditions: • Fixed and adaptive CPU • Fixed and adaptive software • Foreman sequence

  35. Fixed vs Adaptive Systems 35 • Adaptive hardware saves 70% over fixed system • Adaptive application saves • 30% on fixed hardware • 20% on adaptive hardware (total savings of 80%) 30.49 CPU 30 Fixed System Net 25 21.23 Energy (J) Adaptive S/W 20 Adaptive H/W 15 10 7.36 Adaptive Sys 6.25 5 0

  36. Algorithm Comparison • Baseline: Fixed software, adaptive hardware • Adaptive software: • Adaptive DCT/motion thresholds • Instruction, byte count for next frame predicted • Oracle • Instruction and byte count for next frame exact • Adapt-Once • Adapt once at start of encoding • Minimize total energy across entire sequence

  37. Algorithm Comparison 8 7.36 CPU 6.55 6.25 6.09 Fixed 6 Net Energy (J) Adapt Once 4 Adaptive 2 Oracle 0 • Energy consumption of Adaptive within 3% of Oracle • Simple predictors sufficient for energy savings • Adaptive saves 5% over Adapt-Once • Frame-by-frame adaptation can save energy

  38. Other test cases • Low Power CPU • Network energy dominated • Software adaptation did not save energy • Carphone • Little inter-frame variation • One-shot adaptation was sufficient • Adapt-Once, Adaptive, Oracle same energy • Adaptive software saved ~15%

  39. Conclusions • A new framework for coordinated CPU/application adaptation • Combined benefits of both adaptations • Preserves separation between layers • Adaptive applications save energy: • Up to 20% on adaptive hardware • Up to 30% on fixed hardware

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