Power to the People : Leveraging Human Physiological Traits for Microprocessor Frequency Control

1. Alex Shye, Yan Pan, Ben Scholbrock, J. Scott Miller, Gokhan Memik, Peter A. Dinda, Robert P. Dick Northwestern University, EECS Power to the People: Leveraging Human Physiological Traits for Microprocessor Frequency Control ESP Project: http://www.empathicsystems.org International Symposium on Microarchitecture, November 11, 2008. Lake Como, Italy.

2. Summary Claim: Any optimization ultimately exists to satisfy the user Summary of Findings/Contributions • Make a case for adding biometric input devices to future architectures • Show that biometric devices can be used to indicate changes in user satisfaction as performance is altered • Demonstrate that these devices can be leveraged for user-aware optimization Observation: Architectures largely ignore the individual user International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

3. Why care about the user? User-centric applications Optimization opportunity Architectural trade-offs exposed to the user 3 1 2 User variation = optimization potential International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

4. Typical User Interaction User Direction (from keyboard, mouse,etc.) Output (from display,speakers,etc.) International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

5. From the computer’s perspective ? Performance Level Without the appropriate information, it is difficult (if not impossible) for the computer to take the user into account International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

6. Our Goal Leverage human physiological traits for user-aware optimization Provide computer user-related information with biometric inputs 2 1 Physiological traits (biometric inputs) Informed Performance Level International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

7. Biometric Input Devices • Hypothesis: A change in human state due to changes in performance should be reflected by a change in physiological traits • We explore using three biometric devices: • Eye tracker • Galvanic skin response (GSR) sensor • Force sensors International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

8. Eye Tracker • Process video feed for: • Pupil radius • X-Y Coordinates of pupil on video • 2 measurements: • PupilRadius • Mental workload [Iqbal CHI2005] • Perceptual changes [Einhauser NAS 2008] • Emotion processing [Partala JHCS 2003] • PupilMovement • Event Perception [Smith ETRA 2006] International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

9. Galvanic Skin Response (GSR) • Conductance of skin • Reflects “fight-or-flight” response • Increases with engagement • Decreases with relaxation International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

10. GSR Behavior • GSR spikes with interest • DeltaGSR metric measures only the increases in GSR International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

11. Force Sensors • Piezoresistive Force Sensors • Conductance α Force • MaxArrow • = Max(4 sensors) International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

12. Sensor Selection • They do not impede with the computer use • Require little effort to activate/mount • Can be easily integrated • Laptops contain integrated camera for eye tracking • Mouse/keyboard can be enhanced with GSR and force sensors • Power consumption negligible • “Cheap” extensions International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

13. Sensor Metrics • Four measurements: • PupilRadius, PupilMovement, DeltaGSR, MaxArrow • Sample at 30 Hz • Each second, compute three statistics: • Max, Mean, and Variance • Sensor metric = Statistic_Measurement • E.g., Max_MaxArrow and Mean_PupilRadius International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

14. User Study Setup • IBM Thinkpad T61 • Intel Core 2 Duo CPU supporting Intel Speedstep (DVFS) • 5 Frequencies (2.2Ghz -- 600Mhz) • Windows XP • Three user studies: • First two show that physiological traits change with performance • Third evaluates a system leveraging this information • Compare to an Adaptive DVFS scheme modeled after the Linux ondemand governor • Three interactive applications: • Need for Speed • Tetris Arena (third user study) • Microsoft Word (third user study) International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

15. First User Study • Goal: • Do human physiological traits change with changes in performance? • How: • 14 users • Play Need for Speed • Drop performance to 600Mhz for 20 seconds • At same point in game every time International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

16. Mean_PupilMovement • Decrease of pupil movement across most users International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

17. Max_MaxArrow • Decrease in arrow pressure across most users International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

18. Max_DeltaGSR • Change varies among users • Some get more aroused (irritated) • Some get less aroused (bored) International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

19. Second User Study • Goal: • Can changes in physiological traits be distinguished during game play? • Are the changes correlated to user satisfaction? • How: • 20 users • Play Need for Speed • Randomly change to each of four other frequencies twice • First time, just collect sensor metrics • Second time, ask for user satisfaction rating: 1 (bad) – 5 (good) International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

20. Detecting/Interpreting Changes in Sensor Readings • “Good” sensor metric behavior • If user satisfaction same, sensor metrics should remain same • If user satisfaction different, sensor metrics should reflect this • We develop a T-test-based Similarity Metric • T-test distribution of sensor metric samples from different frequencies • High confidence indicates difference in user satisfaction • Low confidence indicates no change in user satisfaction International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

21. Using the T-test Similarity Metric We adopt an 85% confidence threshold International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

22. Sensor Metrics vs. User Satisfaction • Success: T-test prediction matches change in user satisfaction • False Positive: T-test prediction falsely predicts change • False Negative: T-test prediction falsely predicts no change International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

23. Using Biometrics for Optimization • We have shown that: • Human physiological traits do change with performance • We can use biometric readings to distinguish these changes • We construct PTP to leverage biometric readings • Physiological Traits-based Power-management • Power To the People • Built on top of Adaptive DVFS • Tests physiological traits to find a performance level comfortable for the user (settled frequency) • Uses settled frequency to set a ceiling for Adaptive DVF International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

24. PTP Learning Algorithm • Start at highest frequency • Successively test lower frequencies one by one • Each frequency test consists of three trials • One trial consists of: • 20 seconds at highest frequency, 20 seconds at test frequency • Compute T-test for sensor metrics • Majority vote across sensors • Majority vote across trials • If a majority vote says OK, try next frequency • If majority vote predicts difference, go up one frequency and settle there International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

25. PTP Learning Algorithm International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

26. Third User Study: PTP Evaluation • Goal: • Does PTP work? • How • Run the learning algorithm to find the settled frequency for the individual user • Run once with PTP at the settled frequency and once with the Adaptive scheme • Order is randomized • 2.5 minutes each • Ask for user satisfaction rating from 1 (bad) – 5 (good) • Measure total system power for comparison International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

27. Need for Speed • Slightly decrease user satisfaction • 18% total system power savings International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

28. Tetris • No change to user satisfaction • 33% total system power savings International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

29. Microsoft Word • No change to user satisfaction • 2% total system power savings International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

30. Conclusion • Motivate new biometric input devices for future architectures • Eye tracker, GSR, and force sensors • Human physiological traits change with performance • Show biometric inputs can be used to indicate user satisfaction • Demonstrate PTP for user-aware power management • 18% total system power savings across three applications • Little to no change in user satisfaction International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

31. Thank you! • Questions? Alex Shye http://www.ece.northwestern.edu/~ash451 shye@northwestern.edu ESP: Empathic Systems Project http://www.empathicsystems.org International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy

32. Sensors and User Satisfaction International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy