Human Factors and User Interfaces in Energy Efficiency

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17 Νοε 2013 (πριν από 3 χρόνια και 6 μήνες)

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Human Factors and User Interfaces in
Energy Efficiency

Lin
Zhong

ELEC518, Spring 2011

2

Motivation

Operating system

Application

Software

Hardware

User interface

User

Processor

Memory

Massive
storage

Network
interface

Display &
other interface
hardware

3

Energy efficiency: definition

Energy efficiency =

User productivity

Avg. power consumption

= (User productivity)
×
(Power efficiency)

Human
-
computer interaction
(HCI)

Low
-
power design

4

Limits


Minimal power/energy requirements



Human speeds

5

Speed mismatch

1
10
100
1000
10000
100000
1000000
1968
1972
1976
1980
1984
1988
1992
1996
2000
2004
Year
Times of improvement
Olympic Gold Metal winner: 100m dash (men)
Olympic Gold Metal winner: 100m dash (women)
# of transistors for Intel processor
Processor performance measured in MIPS
A constantly slow user

An increasingly powerful computer

Sources: intel.com and
factmonster.com

6

Slow
-
user problem

0
0.2
0.4
0.6
0.8
1
0
1
2
3
4
5
Time (s)
Power (Watt)
A computer spends most of its energy in
interfacing

Slow
-
user problem cannot be alleviated by
a “better” or more powerful interface

Model Human Processor

Cognitive process

Perceptual process

Motor process

Model Human Processor: Card, Moran & Newell’83

Three processes involved in the
user reaction to a computer

7

Perceptual process


Fixations and saccades


Fixation: information absorbed in
the fovea (60ms)


Saccades: quick movements
between fixations (30ms)


Each GUI object requires one
fixation and one saccade


Rauding rate


Raud: read with understanding


30 letters/second (Carver, 1990)

8

Cognitive process


Hick
-
Hyman Law


N

distinct and equally possible choices




Applicable only to simple cognitive tasks


Selection: menu, buttons, list



(s)

1
N
log
7
1
delay

Cognitive
2


9

General form


Hick
-
Hyman Law



p
i
: the probability that the
ith

choice is selected





p
i

can be estimated based on history


)
1
(1

log
7
1
delay

Cognitive
i
1
p
p
N
i
i




10

Motor process


Stylus operation



Fitts


Law


A
: distance to move


W
: target dimension along the moving direction




Parameters adopted from (MacKenzie and Buxton, 1992)

(s)

)
1
(
log
166
.
0
23
.
0
delay
Motor
2



W
A
11

0
5
10
15
20
25
30
35
40
45
50
0
10
20
30
40
50
Power Law of practice


Speed

on n
th

trial


S
n

=
S
1

n
a
, where a ≈
0
.
4



A
pplies to
perceptual

& motor

processes


D
oes not apply to
cognitive process or quality

Learning curve of text entry using Twiddler, Lyons, 2004

Power Law prediction

Measurement

12

Human capacity limitations

Human capacity


Perceptual


Cognitive


Motor


……

13

14

Cache

Frequent
interactions

Frequently
accessed data

Task to outsource

Interfacing energy

Memory access
latency

Cost to reduce

Computer & user

CPU & memory

Speed mismatch

Interface cache

Memory cache

Alleviate slow
-
user problem with a
“worse” or less powerful interface

15

Interface cache: examples

Flip phones

Average time spent on laptop per day
declined from 11.1 hours to 6.1 hours 5
months after Blackberry deployment

-----
Goldman Sachs Mobile Device Usage
Study

Human thermal comfort

Starner & Maguire, 1999 and Kroemer et al, 1994

16

A hot case: 3
-
Watt Nokia 3120

Phone case temperature will be
40 deg C higher.

Every One Watt increases surface temperature
by about 13 deg C

17

18

Minimal power/energy requirement

D

Ω

Visual and auditory output

E
min


Ω
∙D
2

10
-
13

(Joule)


About
10
-
14

(Joule) for most
handheld usage

Point source

Minimal energy requirement for
1
-
bit change

with irreversible computing

10
-
21

(Joule)

(Landauer, 1961)

19

Insights for power reduction

D

Ω

Point source

P


Ω
∙D
2

η
(
λ
)∙V(
λ
)

η
(
λ
)
: conversion efficiency

from electrical power

V(
λ
)
: relative human

sensitivity factor

Reflective layer
to control
Ω

λ
: wavelength of light/sound

20

Text entry speed (productivity)

150
23
13
15
25
22
12
7
0
20
40
60
80
100
120
140
160
180
Speaking
mini hardware keyboard
Software keyboard with
stylus
Handwriting
Speed (words per minute)
Raw speed
Corrected speed
21

Impact of human factors

0
0.2
0.4
0.6
0.8
1
0
1
2
3
4
5
Time (s)
Power (Watt)
Length of idle periods cannot be significantly reduced

Power consumption in idle periods is dominated by interfacing devices

Using Calculator on
Sharp Zaurus PDA

99% time and 95% energy spent in idle periods during interaction

22

Experimental setup

Intel Xscale 400Mhz

240X320, 16
-
bit color

mic., speaker & headphone jack

Windows

Transflective/back light

Bluetooth

Speech recog.

Linux/Qt

Reflective/front light

Devices

HP iPAQ 4350

Sharp Zaurus SL
-
5600

23

Experimental setup (
Contd.
)

iPAQ H3870

R
s

V
s

V
dd

5V

Host machine

GPIB card

GPIB cable

Agilent 34401A
multimeter

Measurement

200 samples/second

24

0
0.4
0.8
1.2
1.6
0
0.5
1
1.5
Time (s)
Power (W)
Experimental setup (
Contd.
)

0
0.4
0.8
1.2
1.6
0
0.5
1
1.5
Time (s)
Power (W)
Extra energy/power consumption of an event is
obtained through differential measurement

Extra energy consumption by
writing “x”

Write “x” with
stylus/touchscreen

25

Power breakdown

A handheld usually spends
most time being idle but the
display has to be on most
time


If the display is not on, the
speaker subsystem is usually
on

0
1
2
3
4
iPAQ
Zaurus
Power consumption (mW)
Earphone
Speaker
Lighting
LCD
Computing
Basic idle
Computing: carrying out DCT repetitively

26

Energy characterization


Visual interfaces


Graphical user interfaces (GUIs)


Digital camera


Auditory interfaces


Recording/playback


Speech recognition & synthesis


Manual text entry

27

GUIs


Stylus/Touch
-
screen


Most energy/time spent in idle periods


Energy consumed by computing negligible


Task time determines energy consumption


0
0.2
0.4
0.6
0.8
1
0
1
2
3
4
5
Time (s)
Power (Watt)
28

0
0.4
0.8
1.2
1.6
2
1
207
413
619
825
1031
1237
1443
1649
1855
2061
2267
Time (1/206 s)
Power (W)
Speech synthesis & recognition


Infer the behavior of
Voice Command

by
comparing voice recording and power
trace


Computing is not demanding


Used as baseline for comparison


Voice recording

Power trace

29

Comparison: Output

0
1
2
display off
earphone
display on
earphone
display off
loudspeaker
display on
loudspeaker
Different scenarios
r
output
Lighting required for text
Lighting not required for text

Speech is better only when


display is turned off


earphone is used


nighttime usage

iPAQ

spk
txt
rd
spk
P
P
R
R
r


Energy efficiency
ratio

If
r >1
, speech output is more energy
-
efficient

30

Comparison: Text entry

0.1
1
10
100
0
20
40
60
80
100
120
140
160
Speech recog. input rate (cwpm)
r
input
HW MKB-ideal
VKB-ideal
Letter Recog.-ideal
HW MKB
VKB
Letter
If
r >1
, speech recognition is more energy
-
efficient

State of
the art

Near
future

Ideal

31

Comparison: Text entry (Contd.)

0.1
1
10
100
0
20
40
60
80
100
120
140
160
Speech recog. input rate (cwpm)
r
input
HW MKB-No LCD
VKB-No LCD
Letter Recog.-No LCD
HW MKB-No LCD/Night
VKB-No LCD/Night
Letter Recog.-No LCD/Night
Handwriting recognition is inferior to alternatives

Speech recognition can be the most energy
-
efficient

32

Comparison: Command & control


Speech vs. GUI operation



0
1
2
3
4
5
6
7
8
9
1
2
3
4
5
No. of taps
Maximal no. of words per command
Ideal
95% accurate
95% accurate/No LCD
95% accurate/No LCD/Light
Assume each stylus
tapping takes 750ms

Single word voice command is more energy
-
efficient than GUI
operation with 2 taps

33

Observations


User productivity (speed) is critical


energy consumed being idle is significant


Handwriting
-
based text entry is inferior


Speech
-
based text entry can be superior


Turning off display is important


Accuracy


Loudspeaker consumes significant power


Earphone incurs usability issue


Wireless audio delivery not energy
-
efficient


“Computing” usually consumes trivial energy

34

Examples of energy inefficient interfaces

Kyocera KX2325

LG VX 6100

Microsoft Voice
Command 1.01

35

Energy efficiency: definition

Energy efficiency =

User productivity

Avg. power consumption

= (User productivity)
×
(Power efficiency)

Human
-
computer interaction
(HCI)

Low
-
power design

Model of Man


Herbert Simon


Turing Award (1975)


Nobel Prize in Economics (1978)


Human mind is simple; its apparent complexity is
due to the environment’s complexity


Short
-
term memory is fast but small (~7)


Long
-
term memory is unlimited but writing takes time
(10 to 30 seconds)


Retrieval from long
-
term memory is associative and
depends on the storage structure

Bounded rationality


Limitation on ability to plan long behavior
sequences


Tendency to set aspiration levels for each goal


Tendency to operate on goals sequentially
rather than simultaneously


Satisficing

rather than optimizing search
behavior

http://www.princeton.edu/~smeunier/JonesBounded1.pdf