Transcript ASAC : Automatic Sensitivity Analysis for Approximate Computing

ASAC : Automatic Sensitivity
Analysis for Approximate
Computing
Pooja ROY, Rajarshi RAY, Chundong WANG, Weng Fai WONG
National University of Singapore
LCTES 2014
Why Approximate?
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Why Approximate?
Quality of Service
High QoS
QOS Band
Relaxed accuracy
Acceptable QoS
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Exploring Previous Works
Ansel et.al. (CGO’11)
Algorithm
Sampson et.al. (PLDI’11)
Carbin et.al. (ISSTA’10)
Baek et.al. (PLDI’10)
Carbin et.al.
(OOPSLA’13)
Misailovic et.al. (TECS’13)
Programming (API)
Zhu et.al. (POPL’12)
Compilation
Sidiroglou-Douskos et.al. (FSE’11)
Esmaeilzadeh et.al. (ASPLOS’12)
Hoffman et.al. (ASPLOS’11)
Gupta et.al. (ISLPED’11)
Sampsin et.al. (MICRO’13)
Venkataramani et.al. (MICRO’13)
Chippa
et.al.
(DAC’10)
Kahng et.al. (DAC’12)
ASAC : Automatic Sensitivity Analysis for Approximate Computing
Architecture
Circuit
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Exploring Previous Works
Ansel et.al. (CGO’11)
Algorithm
Sampson et.al. (PLDI’11)
Carbin et.al. (ISSTA’10)
Baek et.al. (PLDI’10)
Carbin et.al.
(OOPSLA’13)
Misailovic et.al. (TECS’13)
Programming (API)
Zhu et.al. (POPL’12)
Compilation
Sidiroglou-Douskos et.al. (FSE’11)
Esmaeilzadeh et.al. (ASPLOS’12)
Hoffman et.al. (ASPLOS’11)
Gupta et.al. (ISLPED’11)
Sampsin et.al. (MICRO’13)
Venkataramani et.al. (MICRO’13)
Chippa
et.al.
(DAC’10)
Kahng et.al. (DAC’12)
ASAC : Automatic Sensitivity Analysis for Approximate Computing
Architecture
Circuit
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Approximation based Programming Paradigm
 New programming paradigm
 Explicit classification of program data
(variables, methods etc.)
Code
Compilation
framework to
support
approximation
Approximable
data
Nonapproximable
data
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Need of Automation
Original
Code
Rewrite
using new
language
constructs
• Programmer’s
Annotation,
• Provision of multiple
versions
Code
Compilation
framework to
support
approximation
Approximable
data
Nonapproximable
data
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Need of Automation
 Writing ‘binutils’ from scratch?
 Expect app developers to provide many
versions?
 Recompile and test ‘Picassa’, ‘VLC,’ with
multiple QoS requirements?
 Providing for entire android/ios kernels?
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Our Approach : ASAC
 Automatic Sensitivity Analysis
 Statistical perturbation based framework
 Scalable
 Specifically, considers internal program
data for approximation
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Key Idea
Code
Perturb each
Variable
Perturbed
Output
Acceptable
QoS
sensitivity
 Sensitivity  particular variables’ contribution towards
the output
 Based on ‘sensitivity’ the variables are ranked
 Low ranked variables can be approximated
 Higher ranked variables are critical
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Key Idea
Code
Perturb each
Variable
Perturbed
Output
Acceptable
QoS
sensitivity
 How to systematically perturb the variables?
 How to translate the perturbed output to sensitivity
ranking?
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Hyperbox Sampling
int sum(){
int i;
double a = 0.1, sum = 0.0;
for(i=0;i<10;i++){
sum += a/10;
}
return sum;
}
i
a
sum
Creating hyperbox with value range of each variable
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Hyperbox Sampling
int sum(){
int i;
double a = 0.1, sum = 0.0;
for(i=0;i<10;i++){
sum += a/10;
}
return sum;
}
i
a
sum
Discretizing each dimension by ‘k’
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Hyperbox Sampling
int sum(){
int i;
double a = 0.1, sum = 0.0;
for(i=0;i<10;i++){
sum += a/10;
}
return sum;
}
i
a
sum
Choosing samples based on “Latin Hyperbox Sampling”
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Hyperbox Sampling
int sum(){
int i;
double a = 0.1, sum = 0.0;
for(i=0;i<10;i++){
sum += a/10;
}
return sum;
}
i
a
sum
0.2
3
0.7
Controlled perturbation
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Perturbed Outputs
 Rule 1
 For a program with ‘n’ variables, discretization
constant ‘k’ and ‘m’ randomly chosen points ,
number of perturbed outputs are (k1)
n1
m* (  (k-i) )
i=0
Not trivial!
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Key Idea
Code
Perturb each
Variable
Perturbed
Output
Acceptable
QoS
sensitivity
 How to systematically perturb the variables?

 How to translate the perturbed output to sensitivity
ranking?
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Perturbed Outputs
 ‘good’ sample – within QoS band
 ‘bad’ sample – outlies the QoS band
(cumulative distribution function (cdf) for each variable)
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Hypothesis Testing
 Kolmogorov-Smirnov test calculates the
max distance between the curves
 Rule 2
 The maximum distance between the curves
is the sensitivity score for the variable.
Higher the score, the more the variable
contributes towards the program output.
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Approximable vs. Critical
 Sensitivity score ( > 0.5) is critical
 For evaluation
 Mild Error Injection : 1/3 (or 1/2) of approximable
variables
 Medium Error Injection : 1/6 of approximable
variables
 Aggressive Error Injection : All of the approximable
variables
 Programs
 SciMark2
 MiBench (JPEG)
 SPEC2006 (464.H264ref)
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ASAC Correctness
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ASAC Correctness
Bench
marks
True
Positive
False
Positive
False
Negative
True
Negative
Precision
Recall
Accuracy
SOR
5
0
1
2
0.83
1
0.88
SMM
1
0
1
6
0.50
1
0.88
Monte
2
0
1
2
0.67
1
0.80
FFT
15
2
2
12
0.88
0.88
0.87
LU
7
1
1
5
0.88
0.88
0.86
Average 0.75
0.95
0.86
*as compared to ‘manually annotated baseline’ (EnerJ, PLDI’11)
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ASAC : JPEG
Input
Encode (Mild)
Decode (Mild)
Encode (Aggressive) Decode (Aggressive)
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ASAC : H264
Error Rate
SNR_Y
SNR_U
SNR_V
BitRate
No Error
36.67
40.74
42.32
149.62
Mild
36.69
37.64
37.65
146.6
Medium
34.05
36.92
36.79
147.12
Aggressive
29.78
32.89
32.99
146.03
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ASAC Runtime
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ASAC Sanity Check
• JPEG : Encode and Decode with error injected in
variables marked as ‘non-approximable’
• H264 – Application crash
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Concluding
 ASAC
 Automatic classification of approximable and
non-approximable data
 Scalable
 No profiling
 Can be applied to program without available
source code
 Approximation
 Saves energy and without performance loss
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Thank you
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