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

Alex Shye, Yan Pan, Ben Scholbrock, J. Scott Miller, Gokhan Memik, Peter A. Dinda, Robert P. Dick

Northwestern University, EECS

International Symposium on Microarchitecture, November 11, 2008. Lake Como, Italy.

ESP Project: http://www.empathicsystems.org

Summary of Findings/Contributions1. Make a case for adding biometric input devices to future

architectures

2. Show that biometric devices can be used to indicate changes in user satisfaction as performance is altered

1. Demonstrate that these devices can be leveraged for user-aware optimization

Claim: Any optimization ultimately exists to satisfy the user

Observation: Architectures largely ignore the individual user

International Symposium on Microarchitecture (MICRO-41), Lake Como, Italy11/11/08 2

22Architectural trade-offsexposed to the user

11User-centric applications

33Optimization opportunityUser variation = optimization potential


User Direction (from keyboard, mouse,etc.)

Output (from display,speakers,etc.)


Without the appropriate information, it is difficult (if notimpossible) for the computer to take the user into accountWithout the appropriate information, it is difficult (if notimpossible) for the computer to take the user into account

Performance Level

????


Physiological traits (biometric inputs)

11Provide computer user-relatedinformation with biometric inputs

Performance LevelInformed

22Leverage human physiological traitsfor user-aware optimization


We explore using three biometric devices:1. Eye tracker2. Galvanic skin response (GSR) sensor3. Force sensors


Hypothesis: A change in human state due to changes in performance should be reflected by a change in physiological traits

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]


Conductance of skin

Reflects “fight-or-flight” response Increases with

engagement Decreases with

relaxation


GSR spikes with interest

DeltaGSR metric measures only the increases in GSR


Piezoresistive Force Sensors

Conductance α Force

MaxArrow = Max(4

sensors)


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


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


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)


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


Decrease of pupil movement across most users


Decrease in arrow pressure across most users


Change varies among users Some get more aroused (irritated) Some get less aroused (bored)


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)


“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


We adopt an85% confidencethreshold

We adopt an85% confidencethreshold


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


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


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


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


Slightly decrease user satisfaction 18% total system power savings


No change to user satisfaction 33% total system power savings


No change to user satisfaction 2% total system power savings


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


Questions?

Alex Shyehttp://www.ece.northwestern.edu/[email protected]

ESP: Empathic Systems Projecthttp://www.empathicsystems.org


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

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