Processor Frequency Setting for Energy Minimization of Streaming Multimedia Application

1. Processor Frequency Setting for Energy Minimization of Streaming Multimedia Application by A. Acquaviva, L. Benini, and B. Riccò, in Proc. 9th Internation Symposium on Hardware/Software Codesign, Apr. 2001.

2. Agenda • Introduction • Power optimization model derivation • Power optimization algorithm • Experimental results • Conclusion

3. Introduction • With technology enhancement, multimedia capabilities are being added to handheld devices. Examples: • Picture taking • MP3 audio playback • Video playback • Audio recording • A new problem arises  power management

4. Introduction • Power management options • Shut down devices when in idle mode • Problem: Background tasks have to be stopped as well • Better approaches: clock frequency and voltageregulation • Lowers system speed in idle states • Reduces LCD display brightness

5. Introduction • Real-time media streaming applications • Retrieve stream data from off-CPU interface (e.g. discs, memory cards) • Process data (e.g. decoding, decompression) • Deliver processed data to output interface (e.g. display, speakers)

6. Power Optimization Model Derivation • System settings: • The CPU must communicate with relatively slower I/O interfaces • Clock frequency can be adjusted by software • Frame-based media (e.g. MP3 audio, MPEG video)

7. Power Optimization Model Derivation • Power consumption: • Energy per frame: V – supply voltage, C – switched capacitance, f – CPU clock frequency, Tf– frame processing time, Nf – number of cycles to process frame, t – cycle time

8. Power Optimization Model Derivation • Due to speed difference between the CPU and external hardware: where Nidle is a non-decreasing function of f • We now have

9. Power Optimization Model Derivation • To satisfy real-time synchronization constraint: Tmax – maximum allowed time for a frame (e.g. 1/30s in 30fps movie)

10. Power Optimization Model Derivation • In words, target of power optimization is to reduce the term Nidle in under the constraint

11. Power Optimization Model Derivation • This technique is more effective if application requires much memory access • However, delay and energy spent for frequency adjustment must also be considered

12. Proposed Algorithm • Define three curves FRB(f), FRA(f), FRW(f), the best-case, average-case and worst-case frame rate at f. • Compute curves for all bit rate and sampling rate values and obtain FRoB(f), FRoA(f), FRoW(f) • Compute FRreq by • Nsample : samples per frame, fixed at 576 for MP1 and MP1 phase 2

13. Proposed Algorithm • Normalize the curves FRoB(f), FRoA(f), FRoW(f) by FRoA(fmax) from a pre-calculated look-up table • Intersect FRreq with three curves to obtain fmin, fav and fmax.

14. Proposed Algorithm • CPU frequency can be set to: • fmin if we find constantly frames processed faster than the average rate • favif we want continuous playback, with some buffering storage for decoding rate jitter • fmax to guarantee real-time performance on a frame-by-frame basis • Greater than fmax if the processor is not dedicated to the application only

15. Experimental Results • Energy consumption per frame

16. Experimental Results • Energy consumption for 16KHz, 16KBit/s audio

17. Experimental Results • Frequency setting

18. Example Calculation • An audio stream of 16KHz, 16KBit/s • Without any optimization, Ef = 10.989mJ • FRreq = 16000 / 576 = 27.78 fps • FRA(fmax) = 65.36 fps • Normalized FR = 27.8 / 65.36 = 0.425 fps • fmin = 85.7MHz, fmax = 106.7MHz • Choosing fmax, Ef = 8.9mJ  19% energy reduction

19. Conclusion • Frequency-energy relationship is derived • An energy optimization algorithm is proposed • Experiment shows dramatic save in power consumption