SlideShare a Scribd company logo
Towards functional Safety
Compliance of Matrix-Matrix
Multiplication for Machine
Learning-based Autonomous
systems
Javier Fernández* , Jon Perez*, Irune Agirre*, Imanol Allende*, Jaume
Abella, Francisco J. Cazorla
( ) (*)
CONTENTS
01
02
03
CONTENTS
CONTEXT
PROPOSED SOLUTION
EVALUATION
04 CONCLUSIONS
© 2021 IKERLAN. All rights reserved
Introduction
CONTEXT
IEC 61508 IEC 61513 EN 5012X ISO 26262
Current Standards
“Process where a machine/computer/system learns things that can be
used to make it perform better in the future 2 ”
ML is based on probabilistic models built from training
data set instead from specifications for making
predictions and for making decisions
ML has made enormous progress reaching near-human
accuracy in several safety-related tasks.
ML algorithms process large data volumes and they
need higher performance than that provided by
traditional dependable embedded systems
Machine learning (ML)
[1] IEC 61508 (1-7): Functional safety of
E/E/PE safety related systems
[2] Data Mining: Practical Machine Learning Tools and Techniques
• ISO/PAS 21448:2019 (SOTIF)
• ANSI/ UL 4600
• VDE-AR-E 2842-61
New standards
3
© 2021 IKERLAN. All rights reserved
Introduction
Object
Detection
CONTEXT
1. General Matrix-Matrix Multiplication (GEMM)
2. CUBLAS
3. Processor-specific variants such as AVX
MMM offered by YOLO
YOLO (You Only Look Once):
• Multiscale Object detector
• Based on darknet
Library
Matrix-Matrix Multiplication (MMM):
• 67 % execution time [1]
• Backbone of the
Convolutional Neural
Networks (CNNs)
Code subset
[1] Evaluation and mitigation of soft-errors in neural network
based object detection in three GPU architectures
4
© 2021 IKERLAN. All rights reserved
Failures &
Diagnostic
Coverage
CONTEXT
Introduction
Object
Detection
“DC denotes the effectiveness of
diagnostic techniques to detect
dangerous errors” [1]
Diagnostic Coverage (DC)
a) Permanent Faults
b) Intermittent Faults
c) Transient Faults
Classification of faults (frequency)
0 1 0
0 0 1
1 0 1
1 1 0
0 0 1
1 0 0
Activation weights
to be mapped
Activation weights
mapped Systematic: associated with the
development process
Random: Associated to hardware
errors. For example:
• Electromagnetic interference
• Component wear-out
• Voltage drops
Classification of faults (source)
5 [2] Understanding Error Propagation in Deep Learning
Neural Network (DNN) Accelerators and Applications
[2] [2]
[1] Evaluation Multi-core devices for safety-critical systems:
a survey
CONTENTS
01
02
03
04
CONTENTS
CONTEXTUALIZATION
PROPOSED SOLUTION
EVALUATION
CONCLUSIONS
© 2021 IKERLAN. All rights reserved
Error avoidance
PROPOSED SOLUTION
MMM: VIOLATIONS BY RULE OF MISRA C:2012
DARKNET: VIOLATIONS BY RULE OF MISRA C:2012
• Usage of defensive programming
• Compliance with codification guidelines (MISRA C [1])
Strategy
• Not explicitly defining types with the size and signedness for
basic numerical types.
• Not checking the correctness of input parameters
• Not explicitly defining the desired precedence of operators
within expressions
• Not explicitly defining input parameter pointers as
const-qualified type
Violations Sequential MMM
7
[1] MISRA C:2012 – Guidelines for the use of the C language
in critical systems
• Polyspace
Analysis Tool
© 2021 IKERLAN. All rights reserved
Error Detection
Error avoidance
To guarantee a safe execution during the software
deployment of the Matrix-matrix multiplication (MMM)
through the use of diagnostic techniques.
Objective
a) To employ checksums algorithms as diagnostic
techniques to compute an Execution Signature (ES)
of all the values of the input and output matrices.
b) To provide a catalogue of checksums and to evaluate
a trade-off between DC and performance impact.
Proposal
• XOR
• Twos’ complement
• Ones’ complement
Checksums algorithms[1]
• Fletcher
• CRC
𝐴11 𝐴12
𝐴21 𝐴22
𝐴31 𝐴32
𝑋
𝐵11 𝐵12 𝐵13
𝐵21 𝐵22 𝐵23
=
𝐶11 𝐶12 𝐶13
𝐶21 𝐶22 𝐶23
𝐶31 𝐶32 𝐶33
PROPOSED SOLUTION
[1] The Effectiveness of Checksums for 8
© 2021 IKERLAN. All rights reserved
Error Detection
Error avoidance
a) Periodic diagnosis with design time fixed data pattern(s)
b) Redundancy (with or without diversity)
Safety architectural patterns
Control
action
Inputs
(camera/simulation)
Switch every
N executions
ES from MMM including
checksums
Majority voter
MMM
including
checksums
Compare every N
executions
Diagnostic
Known inputs
Known ES
9
Voted Output
PROPOSED SOLUTION
ES from MMM including
checksums
ES from MMM including
checksums
CONTENTS
01
02
03
04
CONTENTS
CONTEXTUALIZATION
PROPOSED SOLUTION
EVALUATION
CONCLUSIONS
© 2021 IKERLAN. All rights reserved
EVALUATION
Set-up
Sequential MMM:
• Core R5 implemented in Zynq UltraScale+
AVX-based MMM:
• Core i7 implemented in a PC
Implementation
Square matrices:
80x80, 160x060 and 320x320
Unbalanced matrices:
𝑀 = 18; 𝑁 = 230400; K =64
Performance experiments
Square matrices:
20x20, 40x40 and 80x80
Unbalanced matrices:
𝐿1 ∶ 𝑀 = 32; 𝑁 = 29; K = 144
𝐿2 ∶ 𝑀 = 8; 𝑁 = 900; K = 8
𝐿3 ∶ 𝑀 = 15; 𝑁 = 225; K = 48
𝐿59: 𝑀 = 18; 𝑁 = 900; K =1024
DC experiments
Unbalanced matrices:
Kind of matrices
𝐴11 𝐴12
𝐴21 𝐴22
𝐴31 𝐴32
𝑋
𝐵11 𝐵12 𝐵13
𝐵21 𝐵22 𝐵23
=
𝐶11 𝐶12 𝐶13
𝐶21 𝐶22 𝐶23
𝐶31 𝐶32 𝐶33
𝑀𝑥𝐾 𝑀𝑥𝑁
𝐾𝑥𝑁
Square matrices: Dimension N x N
11
© 2021 IKERLAN. All rights reserved
Performance
Impact (PI)
Set-up
EVALUATION
Performance Impact (PI):
𝑛 =
𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒 𝑋
𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒𝑌
Definition
𝑋 = 𝐶𝑜𝑑𝑒 𝑎𝑐𝑐𝑜𝑚𝑝𝑙𝑖𝑠ℎ𝑖𝑛𝑔 𝑀𝐼𝑆𝑅𝐴 𝐶
𝑌 = 𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑐𝑜𝑑𝑒
Adoption of MISRA C
𝑋 = 𝑐𝑜𝑑𝑒 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑒𝑑 𝑎𝑛𝑑 𝑎𝑐𝑐𝑜𝑚𝑝𝑙𝑖𝑠ℎ𝑖𝑛𝑔 𝑀𝐼𝑆𝑅𝐴 𝐶
𝑌 = 𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑐𝑜𝑑𝑒
Adoption of MISRA C and code optimization
𝑋 = 𝐶𝑜𝑑𝑒 𝑖𝑛𝑐𝑙𝑢𝑖𝑑𝑖𝑛𝑔 𝑐ℎ𝑒𝑐𝑘𝑠𝑢𝑚/𝑠
𝑌 = 𝐶𝑜𝑑𝑒 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑒𝑑 𝑎𝑛𝑑 𝑎𝑐𝑐𝑜𝑚𝑝𝑙𝑖𝑠ℎ𝑖𝑛𝑔 𝑀𝐼𝑆𝑅𝐴 𝐶
Experiments
𝑛 = 1.01
𝑛 = 0.95
12
© 2021 IKERLAN. All rights reserved
Performance
Impact (PI)
Set-up
EVALUATION
Sequential MMM
Remarks
1. Decrement in the PI when the size of the matrices increase,
approaching to asymptotically specific values
2. Individual experiments confirm the increase in performance
impact from the less intricate algorithms (XOR, 1’s
complement, 2’s complement) to even more intricate ones
(Fletcher, CRC).
3. The PI is smaller on the most external loops (M,E) with
respect to the internal loop (I)
13
© 2021 IKERLAN. All rights reserved
Performance
Impact (PI)
Set-up
EVALUATION
Sequential MMM
AVX-based MMM
1. Same tendency to a fixed PI value when the size of the
matrices increase.
2. PI Increment in the Fletcher and 1’s complement checksums
according to the results obtained in the sequential MMM
experiments.
Reasons: Lack of specific arithmetic operations in AVX
• 1’s complement requires adding all values to be checked and
subsequently adding the carry bit back into the result.
• Fletcher requires a modulo operation
Remarks
14
© 2021 IKERLAN. All rights reserved
EVALUATION
Set-up
Performance
Impact (PI)
Diagnostic
Coverage
(DC)
• Induction of an exhaustive single-bit error injection in
all bit positions of the values of matrices A and B
𝐷𝐶 =
𝐷𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝑓𝑎𝑢𝑙𝑡𝑠
𝐼𝑛𝑗𝑒𝑐𝑡𝑒𝑑 𝑓𝑎𝑢𝑙𝑡𝑠
𝑥 100 (%)
• DC is independent of the platform where is
implemented
• The percentage of the code protected is dependent
of the loop where the checksum is implemented
Fault injection experiment A) External loop injection experiment
𝐴11 𝐴12
𝐴21 𝐴22
𝐴31 𝐴32
𝑋
𝐵11 𝐵12 𝐵13
𝐵21 𝐵22 𝐵23
=
𝐶11 𝐶12 𝐶13
𝐶21 𝐶22 𝐶23
𝐶31 𝐶32 𝐶33
B) Intermediate loop injection experiment
𝐴11 𝐴12
𝐴21 𝐴22
𝐴31 𝐴32
𝑋
𝐵11 𝐵12 𝐵13
𝐵21 𝐵22 𝐵23
=
𝐶11 𝐶12 𝐶13
𝐶21 𝐶22 𝐶23
𝐶31 𝐶32 𝐶33
C) Internal loop injection experiment (100 %)
𝐴11 𝐴12
𝐴21 𝐴22
𝐴31 𝐴32
𝑋
𝐵11 𝐵12 𝐵13
𝐵21 𝐵22 𝐵23
=
𝐶11 𝐶12 𝐶13
𝐶21 𝐶22 𝐶23
𝐶31 𝐶32 𝐶33
15
© 2021 IKERLAN. All rights reserved
EVALUATION
Set-up
Performance
Impact (PI)
Diagnostic
Coverage
(DC)
DC for Sequential MMMs
1. XOR, 1’s and 2’s complements not reach 100% DC in the
internal loop.
2. DC is highly dependent on the dimension of the matrix
involved in the MMM
3. All the checksums combinations reach 100% DC except for
XOR_Fletcher and Two’s_Fletcher
4. Evaluation of a layer extracted from YOLO:
1. 1’ complement -> 98.5 %
2. 2’s complement -> 96.9 %
Remarks
16
© 2021 IKERLAN. All rights reserved
EVALUATION
Set-up
Performance
Impact (PI)
Diagnostic
Coverage
(DC)
DC for AVX MMMs
1. DC of XOR (all loops) and 1’s (E), 2’s
complement (E), CRC (I) and Fletcher (I)
checksums remain the same value.
2. Increment in the DC of the rest of checksum.
Reason:
AVX instructions compute the ES of 8 data
values in each iteration and therefore the
percentage of protected data is higher than in
the sequential implementation.
Remarks
DC for Sequential MMMs
17
© 2021 IKERLAN. All rights reserved
Set-up
Performance
Impact (PI)
Diagnostic
Coverage
(DC)
Trade-off
PI vs DC
EVALUATION
18
1. All combination of checksums reach 100%
DC except for XOR_Fletcher and TWO’s
Fletcher
2. Checksums that reach 100% DC:
• Fletcher (I)
• CRC (I)
• XOR_CRC
• ONE’s Fletcher
• Fletcher_CRC
Remarks
© 2021 IKERLAN. All rights reserved
EVALUATION
Set-up
Performance
Impact (PI)
Diagnostic
Coverage
(DC)
Trade-off
PI vs DC
19
1. Checksums that reach 100% DC:
• Fletcher (I)
• CRC (I)
• Al combinations of checksums
Remarks
CONTENTS
01
02
03
04
CONTENTS
CONCLUSIONS
CONTEXTUALIZATION
PROPOSED SOLUTION
EVALUATION
© 2021 IKERLAN. All rights reserved
Conclusions & Future Work
21
Adaptation to coding guidelines
Trade-off between DC and PI
Future work
It doesn’t require a huge effort and the PI is negligible (lower than 1%) • DC evaluation with multiple bit errors
instead of single bit-errors
• Extend this work towards platform
accelerators such as GPUs and FPGAs
Achievement of 100% of DC with a PI between n = 1.001 and n=2.97 for the
largest matrix sized in the Sequential implementation and n = 1.001 and n=
6.70 in AVX-based implementation
High dependence of the performance impact
with respect to the dimension of the matrices.
The selection of the most appropriate checksums combination depends on
the dimensions of the matrix under consideration
CONCLUSIONS
IKERLAN
P.º José María Arizmendiarrieta, 2 - 20500 Arrasate-Mondragón
T. +34 943712400 F. +34 943796944
THANK YOU
IKERLAN
P.º José María Arizmendiarrieta, 2 - 20500 Arrasate-Mondragón
T. +34 943712400 F. +34 943796944
NAME: JAVIER FERNÁNDEZ MUÑOZ
EMAIL: JAVIER.FERNANDEZ@IKERLAN.ES

More Related Content

What's hot

Automated Testing of Autonomous Driving Assistance Systems
Automated Testing of Autonomous Driving Assistance SystemsAutomated Testing of Autonomous Driving Assistance Systems
Automated Testing of Autonomous Driving Assistance Systems
Lionel Briand
 
Testing of Cyber-Physical Systems: Diversity-driven Strategies
Testing of Cyber-Physical Systems: Diversity-driven StrategiesTesting of Cyber-Physical Systems: Diversity-driven Strategies
Testing of Cyber-Physical Systems: Diversity-driven Strategies
Lionel Briand
 
Testing Machine Learning-enabled Systems: A Personal Perspective
Testing Machine Learning-enabled Systems: A Personal PerspectiveTesting Machine Learning-enabled Systems: A Personal Perspective
Testing Machine Learning-enabled Systems: A Personal Perspective
Lionel Briand
 
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
ijceronline
 
Scalable and Cost-Effective Model-Based Software Verification and Testing
Scalable and Cost-Effective Model-Based Software Verification and TestingScalable and Cost-Effective Model-Based Software Verification and Testing
Scalable and Cost-Effective Model-Based Software Verification and Testing
Lionel Briand
 
Metamorphic Security Testing for Web Systems
Metamorphic Security Testing for Web SystemsMetamorphic Security Testing for Web Systems
Metamorphic Security Testing for Web Systems
Lionel Briand
 
OCLR: A More Expressive, Pattern-Based Temporal Extension of OCL
OCLR: A More Expressive, Pattern-Based Temporal Extension of OCLOCLR: A More Expressive, Pattern-Based Temporal Extension of OCL
OCLR: A More Expressive, Pattern-Based Temporal Extension of OCL
Lionel Briand
 
System Testing of Timing Requirements based on Use Cases and Timed Automata
System Testing of Timing Requirements based on Use Cases and Timed AutomataSystem Testing of Timing Requirements based on Use Cases and Timed Automata
System Testing of Timing Requirements based on Use Cases and Timed Automata
Lionel Briand
 
Detecting soft errors by a purely software approach
Detecting soft errors by a purely software approachDetecting soft errors by a purely software approach
Detecting soft errors by a purely software approach
Md. Hasibur Rashid
 
Efficient decentralized iterative learning tracker for unknown sampled data i...
Efficient decentralized iterative learning tracker for unknown sampled data i...Efficient decentralized iterative learning tracker for unknown sampled data i...
Efficient decentralized iterative learning tracker for unknown sampled data i...
ISA Interchange
 
Adaptive fault tolerance in real time cloud_computing
Adaptive fault tolerance in real time cloud_computingAdaptive fault tolerance in real time cloud_computing
Adaptive fault tolerance in real time cloud_computing
www.pixelsolutionbd.com
 
Integrating Adaptation Mechanisms Using Control Theory Centric Architecture M...
Integrating Adaptation Mechanisms Using Control Theory Centric Architecture M...Integrating Adaptation Mechanisms Using Control Theory Centric Architecture M...
Integrating Adaptation Mechanisms Using Control Theory Centric Architecture M...
Filip Krikava
 
IRJET- Intrusion Detection based on J48 Algorithm
IRJET- Intrusion Detection based on J48 AlgorithmIRJET- Intrusion Detection based on J48 Algorithm
IRJET- Intrusion Detection based on J48 Algorithm
IRJET Journal
 
Itab innovative assessement tool
Itab innovative assessement toolItab innovative assessement tool
Itab innovative assessement tool
Martin J Ippel
 
Automated Testing of Autonomous Driving Assistance Systems
Automated Testing of Autonomous Driving Assistance SystemsAutomated Testing of Autonomous Driving Assistance Systems
Automated Testing of Autonomous Driving Assistance Systems
Lionel Briand
 
Enabling Automated Software Testing with Artificial Intelligence
Enabling Automated Software Testing with Artificial IntelligenceEnabling Automated Software Testing with Artificial Intelligence
Enabling Automated Software Testing with Artificial Intelligence
Lionel Briand
 
Final Exam Questions Fall03
Final Exam Questions Fall03Final Exam Questions Fall03
Final Exam Questions Fall03
Radu_Negulescu
 
C010241521
C010241521C010241521
C010241521
IOSR Journals
 
Midterm Exam Solutions Fall03
Midterm Exam Solutions Fall03Midterm Exam Solutions Fall03
Midterm Exam Solutions Fall03
Radu_Negulescu
 

What's hot (19)

Automated Testing of Autonomous Driving Assistance Systems
Automated Testing of Autonomous Driving Assistance SystemsAutomated Testing of Autonomous Driving Assistance Systems
Automated Testing of Autonomous Driving Assistance Systems
 
Testing of Cyber-Physical Systems: Diversity-driven Strategies
Testing of Cyber-Physical Systems: Diversity-driven StrategiesTesting of Cyber-Physical Systems: Diversity-driven Strategies
Testing of Cyber-Physical Systems: Diversity-driven Strategies
 
Testing Machine Learning-enabled Systems: A Personal Perspective
Testing Machine Learning-enabled Systems: A Personal PerspectiveTesting Machine Learning-enabled Systems: A Personal Perspective
Testing Machine Learning-enabled Systems: A Personal Perspective
 
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
 
Scalable and Cost-Effective Model-Based Software Verification and Testing
Scalable and Cost-Effective Model-Based Software Verification and TestingScalable and Cost-Effective Model-Based Software Verification and Testing
Scalable and Cost-Effective Model-Based Software Verification and Testing
 
Metamorphic Security Testing for Web Systems
Metamorphic Security Testing for Web SystemsMetamorphic Security Testing for Web Systems
Metamorphic Security Testing for Web Systems
 
OCLR: A More Expressive, Pattern-Based Temporal Extension of OCL
OCLR: A More Expressive, Pattern-Based Temporal Extension of OCLOCLR: A More Expressive, Pattern-Based Temporal Extension of OCL
OCLR: A More Expressive, Pattern-Based Temporal Extension of OCL
 
System Testing of Timing Requirements based on Use Cases and Timed Automata
System Testing of Timing Requirements based on Use Cases and Timed AutomataSystem Testing of Timing Requirements based on Use Cases and Timed Automata
System Testing of Timing Requirements based on Use Cases and Timed Automata
 
Detecting soft errors by a purely software approach
Detecting soft errors by a purely software approachDetecting soft errors by a purely software approach
Detecting soft errors by a purely software approach
 
Efficient decentralized iterative learning tracker for unknown sampled data i...
Efficient decentralized iterative learning tracker for unknown sampled data i...Efficient decentralized iterative learning tracker for unknown sampled data i...
Efficient decentralized iterative learning tracker for unknown sampled data i...
 
Adaptive fault tolerance in real time cloud_computing
Adaptive fault tolerance in real time cloud_computingAdaptive fault tolerance in real time cloud_computing
Adaptive fault tolerance in real time cloud_computing
 
Integrating Adaptation Mechanisms Using Control Theory Centric Architecture M...
Integrating Adaptation Mechanisms Using Control Theory Centric Architecture M...Integrating Adaptation Mechanisms Using Control Theory Centric Architecture M...
Integrating Adaptation Mechanisms Using Control Theory Centric Architecture M...
 
IRJET- Intrusion Detection based on J48 Algorithm
IRJET- Intrusion Detection based on J48 AlgorithmIRJET- Intrusion Detection based on J48 Algorithm
IRJET- Intrusion Detection based on J48 Algorithm
 
Itab innovative assessement tool
Itab innovative assessement toolItab innovative assessement tool
Itab innovative assessement tool
 
Automated Testing of Autonomous Driving Assistance Systems
Automated Testing of Autonomous Driving Assistance SystemsAutomated Testing of Autonomous Driving Assistance Systems
Automated Testing of Autonomous Driving Assistance Systems
 
Enabling Automated Software Testing with Artificial Intelligence
Enabling Automated Software Testing with Artificial IntelligenceEnabling Automated Software Testing with Artificial Intelligence
Enabling Automated Software Testing with Artificial Intelligence
 
Final Exam Questions Fall03
Final Exam Questions Fall03Final Exam Questions Fall03
Final Exam Questions Fall03
 
C010241521
C010241521C010241521
C010241521
 
Midterm Exam Solutions Fall03
Midterm Exam Solutions Fall03Midterm Exam Solutions Fall03
Midterm Exam Solutions Fall03
 

Similar to Towards Functional Safety compliance of Matrix-Matrix Multiplication

T1-4_Maslennikov_et_al.pdf
T1-4_Maslennikov_et_al.pdfT1-4_Maslennikov_et_al.pdf
T1-4_Maslennikov_et_al.pdf
MareLunare
 
5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems
Angela Hauber
 
5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems
MEN Micro
 
5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems
MEN Mikro Elektronik GmbH
 
Poster_group22
Poster_group22Poster_group22
Poster_group22
Ahmad Shahir Ismail
 
Detecting soft errors by a purely software approach
Detecting soft errors by a purely software approachDetecting soft errors by a purely software approach
Detecting soft errors by a purely software approach
Md. Hasibur Rashid
 
C010421720
C010421720C010421720
C010421720
IOSR Journals
 
Modeling & Design of Dtmf Technique Based Automatic Mobile Switching & Contro...
Modeling & Design of Dtmf Technique Based Automatic Mobile Switching & Contro...Modeling & Design of Dtmf Technique Based Automatic Mobile Switching & Contro...
Modeling & Design of Dtmf Technique Based Automatic Mobile Switching & Contro...
IRJET Journal
 
Rathinasabapathy
RathinasabapathyRathinasabapathy
Rathinasabapathy
Rathina sabapathy k
 
IRJET- Design and Characterization of MAEC IP Core
IRJET- Design and Characterization of MAEC IP CoreIRJET- Design and Characterization of MAEC IP Core
IRJET- Design and Characterization of MAEC IP Core
IRJET Journal
 
Online and Offline Testing Of C-Bist Using Sram
Online and Offline Testing Of C-Bist Using SramOnline and Offline Testing Of C-Bist Using Sram
Online and Offline Testing Of C-Bist Using Sram
iosrjce
 
R09 design of fault tolerant systems
R09 design of fault tolerant systemsR09 design of fault tolerant systems
R09 design of fault tolerant systems
అనిల్ జూలూరి
 
Lect2 up010 (100324)
Lect2 up010 (100324)Lect2 up010 (100324)
Lect2 up010 (100324)
aicdesign
 
The RaPId Toolbox for Parameter Identification and Model Validation: How Mode...
The RaPId Toolbox for Parameter Identification and Model Validation: How Mode...The RaPId Toolbox for Parameter Identification and Model Validation: How Mode...
The RaPId Toolbox for Parameter Identification and Model Validation: How Mode...
Luigi Vanfretti
 
Fuzzy System Approach for TCSC Based Controller Design
Fuzzy System Approach for TCSC Based Controller DesignFuzzy System Approach for TCSC Based Controller Design
Fuzzy System Approach for TCSC Based Controller Design
IRJET Journal
 
Design of Adaptive Sliding Mode Control with Fuzzy Controller and PID Tuning ...
Design of Adaptive Sliding Mode Control with Fuzzy Controller and PID Tuning ...Design of Adaptive Sliding Mode Control with Fuzzy Controller and PID Tuning ...
Design of Adaptive Sliding Mode Control with Fuzzy Controller and PID Tuning ...
IRJET Journal
 
OPAL-RT Webinar - Challenges in Protection Relay Testing
OPAL-RT Webinar - Challenges in Protection Relay TestingOPAL-RT Webinar - Challenges in Protection Relay Testing
OPAL-RT Webinar - Challenges in Protection Relay Testing
OPAL-RT TECHNOLOGIES
 
vinayak_Resume_18112014
vinayak_Resume_18112014vinayak_Resume_18112014
vinayak_Resume_18112014
vinayak Devkatte
 
Hairong Qi V Swaminathan
Hairong Qi V SwaminathanHairong Qi V Swaminathan
Hairong Qi V Swaminathan
FNian
 
Study of Reliability Analysis to the Iraqi South Region Network
Study of Reliability Analysis to the Iraqi South Region NetworkStudy of Reliability Analysis to the Iraqi South Region Network
Study of Reliability Analysis to the Iraqi South Region Network
IRJET Journal
 

Similar to Towards Functional Safety compliance of Matrix-Matrix Multiplication (20)

T1-4_Maslennikov_et_al.pdf
T1-4_Maslennikov_et_al.pdfT1-4_Maslennikov_et_al.pdf
T1-4_Maslennikov_et_al.pdf
 
5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems
 
5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems
 
5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems5 Techniques to Achieve Functional Safety for Embedded Systems
5 Techniques to Achieve Functional Safety for Embedded Systems
 
Poster_group22
Poster_group22Poster_group22
Poster_group22
 
Detecting soft errors by a purely software approach
Detecting soft errors by a purely software approachDetecting soft errors by a purely software approach
Detecting soft errors by a purely software approach
 
C010421720
C010421720C010421720
C010421720
 
Modeling & Design of Dtmf Technique Based Automatic Mobile Switching & Contro...
Modeling & Design of Dtmf Technique Based Automatic Mobile Switching & Contro...Modeling & Design of Dtmf Technique Based Automatic Mobile Switching & Contro...
Modeling & Design of Dtmf Technique Based Automatic Mobile Switching & Contro...
 
Rathinasabapathy
RathinasabapathyRathinasabapathy
Rathinasabapathy
 
IRJET- Design and Characterization of MAEC IP Core
IRJET- Design and Characterization of MAEC IP CoreIRJET- Design and Characterization of MAEC IP Core
IRJET- Design and Characterization of MAEC IP Core
 
Online and Offline Testing Of C-Bist Using Sram
Online and Offline Testing Of C-Bist Using SramOnline and Offline Testing Of C-Bist Using Sram
Online and Offline Testing Of C-Bist Using Sram
 
R09 design of fault tolerant systems
R09 design of fault tolerant systemsR09 design of fault tolerant systems
R09 design of fault tolerant systems
 
Lect2 up010 (100324)
Lect2 up010 (100324)Lect2 up010 (100324)
Lect2 up010 (100324)
 
The RaPId Toolbox for Parameter Identification and Model Validation: How Mode...
The RaPId Toolbox for Parameter Identification and Model Validation: How Mode...The RaPId Toolbox for Parameter Identification and Model Validation: How Mode...
The RaPId Toolbox for Parameter Identification and Model Validation: How Mode...
 
Fuzzy System Approach for TCSC Based Controller Design
Fuzzy System Approach for TCSC Based Controller DesignFuzzy System Approach for TCSC Based Controller Design
Fuzzy System Approach for TCSC Based Controller Design
 
Design of Adaptive Sliding Mode Control with Fuzzy Controller and PID Tuning ...
Design of Adaptive Sliding Mode Control with Fuzzy Controller and PID Tuning ...Design of Adaptive Sliding Mode Control with Fuzzy Controller and PID Tuning ...
Design of Adaptive Sliding Mode Control with Fuzzy Controller and PID Tuning ...
 
OPAL-RT Webinar - Challenges in Protection Relay Testing
OPAL-RT Webinar - Challenges in Protection Relay TestingOPAL-RT Webinar - Challenges in Protection Relay Testing
OPAL-RT Webinar - Challenges in Protection Relay Testing
 
vinayak_Resume_18112014
vinayak_Resume_18112014vinayak_Resume_18112014
vinayak_Resume_18112014
 
Hairong Qi V Swaminathan
Hairong Qi V SwaminathanHairong Qi V Swaminathan
Hairong Qi V Swaminathan
 
Study of Reliability Analysis to the Iraqi South Region Network
Study of Reliability Analysis to the Iraqi South Region NetworkStudy of Reliability Analysis to the Iraqi South Region Network
Study of Reliability Analysis to the Iraqi South Region Network
 

Recently uploaded

ITSM Integration with MuleSoft.pptx
ITSM  Integration with MuleSoft.pptxITSM  Integration with MuleSoft.pptx
ITSM Integration with MuleSoft.pptx
VANDANAMOHANGOUDA
 
一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理
一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理
一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理
upoux
 
1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf
1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf
1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf
MadhavJungKarki
 
TIME TABLE MANAGEMENT SYSTEM testing.pptx
TIME TABLE MANAGEMENT SYSTEM testing.pptxTIME TABLE MANAGEMENT SYSTEM testing.pptx
TIME TABLE MANAGEMENT SYSTEM testing.pptx
CVCSOfficial
 
AI-Based Home Security System : Home security
AI-Based Home Security System : Home securityAI-Based Home Security System : Home security
AI-Based Home Security System : Home security
AIRCC Publishing Corporation
 
P5 Working Drawings.pdf floor plan, civil
P5 Working Drawings.pdf floor plan, civilP5 Working Drawings.pdf floor plan, civil
P5 Working Drawings.pdf floor plan, civil
AnasAhmadNoor
 
Blood finder application project report (1).pdf
Blood finder application project report (1).pdfBlood finder application project report (1).pdf
Blood finder application project report (1).pdf
Kamal Acharya
 
UNIT 4 LINEAR INTEGRATED CIRCUITS-DIGITAL ICS
UNIT 4 LINEAR INTEGRATED CIRCUITS-DIGITAL ICSUNIT 4 LINEAR INTEGRATED CIRCUITS-DIGITAL ICS
UNIT 4 LINEAR INTEGRATED CIRCUITS-DIGITAL ICS
vmspraneeth
 
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Transcat
 
原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样
原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样
原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样
ydzowc
 
An Introduction to the Compiler Designss
An Introduction to the Compiler DesignssAn Introduction to the Compiler Designss
An Introduction to the Compiler Designss
ElakkiaU
 
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by AnantLLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
Anant Corporation
 
Null Bangalore | Pentesters Approach to AWS IAM
Null Bangalore | Pentesters Approach to AWS IAMNull Bangalore | Pentesters Approach to AWS IAM
Null Bangalore | Pentesters Approach to AWS IAM
Divyanshu
 
SCALING OF MOS CIRCUITS m .pptx
SCALING OF MOS CIRCUITS m                 .pptxSCALING OF MOS CIRCUITS m                 .pptx
SCALING OF MOS CIRCUITS m .pptx
harshapolam10
 
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
nedcocy
 
Height and depth gauge linear metrology.pdf
Height and depth gauge linear metrology.pdfHeight and depth gauge linear metrology.pdf
Height and depth gauge linear metrology.pdf
q30122000
 
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
shadow0702a
 
OOPS_Lab_Manual - programs using C++ programming language
OOPS_Lab_Manual - programs using C++ programming languageOOPS_Lab_Manual - programs using C++ programming language
OOPS_Lab_Manual - programs using C++ programming language
PreethaV16
 
一比一原版(USF毕业证)旧金山大学毕业证如何办理
一比一原版(USF毕业证)旧金山大学毕业证如何办理一比一原版(USF毕业证)旧金山大学毕业证如何办理
一比一原版(USF毕业证)旧金山大学毕业证如何办理
uqyfuc
 
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
Call For Paper -3rd International Conference on Artificial Intelligence Advan...Call For Paper -3rd International Conference on Artificial Intelligence Advan...
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
ijseajournal
 

Recently uploaded (20)

ITSM Integration with MuleSoft.pptx
ITSM  Integration with MuleSoft.pptxITSM  Integration with MuleSoft.pptx
ITSM Integration with MuleSoft.pptx
 
一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理
一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理
一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理
 
1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf
1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf
1FIDIC-CONSTRUCTION-CONTRACT-2ND-ED-2017-RED-BOOK.pdf
 
TIME TABLE MANAGEMENT SYSTEM testing.pptx
TIME TABLE MANAGEMENT SYSTEM testing.pptxTIME TABLE MANAGEMENT SYSTEM testing.pptx
TIME TABLE MANAGEMENT SYSTEM testing.pptx
 
AI-Based Home Security System : Home security
AI-Based Home Security System : Home securityAI-Based Home Security System : Home security
AI-Based Home Security System : Home security
 
P5 Working Drawings.pdf floor plan, civil
P5 Working Drawings.pdf floor plan, civilP5 Working Drawings.pdf floor plan, civil
P5 Working Drawings.pdf floor plan, civil
 
Blood finder application project report (1).pdf
Blood finder application project report (1).pdfBlood finder application project report (1).pdf
Blood finder application project report (1).pdf
 
UNIT 4 LINEAR INTEGRATED CIRCUITS-DIGITAL ICS
UNIT 4 LINEAR INTEGRATED CIRCUITS-DIGITAL ICSUNIT 4 LINEAR INTEGRATED CIRCUITS-DIGITAL ICS
UNIT 4 LINEAR INTEGRATED CIRCUITS-DIGITAL ICS
 
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
 
原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样
原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样
原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样
 
An Introduction to the Compiler Designss
An Introduction to the Compiler DesignssAn Introduction to the Compiler Designss
An Introduction to the Compiler Designss
 
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by AnantLLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
 
Null Bangalore | Pentesters Approach to AWS IAM
Null Bangalore | Pentesters Approach to AWS IAMNull Bangalore | Pentesters Approach to AWS IAM
Null Bangalore | Pentesters Approach to AWS IAM
 
SCALING OF MOS CIRCUITS m .pptx
SCALING OF MOS CIRCUITS m                 .pptxSCALING OF MOS CIRCUITS m                 .pptx
SCALING OF MOS CIRCUITS m .pptx
 
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
 
Height and depth gauge linear metrology.pdf
Height and depth gauge linear metrology.pdfHeight and depth gauge linear metrology.pdf
Height and depth gauge linear metrology.pdf
 
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
 
OOPS_Lab_Manual - programs using C++ programming language
OOPS_Lab_Manual - programs using C++ programming languageOOPS_Lab_Manual - programs using C++ programming language
OOPS_Lab_Manual - programs using C++ programming language
 
一比一原版(USF毕业证)旧金山大学毕业证如何办理
一比一原版(USF毕业证)旧金山大学毕业证如何办理一比一原版(USF毕业证)旧金山大学毕业证如何办理
一比一原版(USF毕业证)旧金山大学毕业证如何办理
 
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
Call For Paper -3rd International Conference on Artificial Intelligence Advan...Call For Paper -3rd International Conference on Artificial Intelligence Advan...
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
 

Towards Functional Safety compliance of Matrix-Matrix Multiplication

  • 1. Towards functional Safety Compliance of Matrix-Matrix Multiplication for Machine Learning-based Autonomous systems Javier Fernández* , Jon Perez*, Irune Agirre*, Imanol Allende*, Jaume Abella, Francisco J. Cazorla ( ) (*)
  • 3. © 2021 IKERLAN. All rights reserved Introduction CONTEXT IEC 61508 IEC 61513 EN 5012X ISO 26262 Current Standards “Process where a machine/computer/system learns things that can be used to make it perform better in the future 2 ” ML is based on probabilistic models built from training data set instead from specifications for making predictions and for making decisions ML has made enormous progress reaching near-human accuracy in several safety-related tasks. ML algorithms process large data volumes and they need higher performance than that provided by traditional dependable embedded systems Machine learning (ML) [1] IEC 61508 (1-7): Functional safety of E/E/PE safety related systems [2] Data Mining: Practical Machine Learning Tools and Techniques • ISO/PAS 21448:2019 (SOTIF) • ANSI/ UL 4600 • VDE-AR-E 2842-61 New standards 3
  • 4. © 2021 IKERLAN. All rights reserved Introduction Object Detection CONTEXT 1. General Matrix-Matrix Multiplication (GEMM) 2. CUBLAS 3. Processor-specific variants such as AVX MMM offered by YOLO YOLO (You Only Look Once): • Multiscale Object detector • Based on darknet Library Matrix-Matrix Multiplication (MMM): • 67 % execution time [1] • Backbone of the Convolutional Neural Networks (CNNs) Code subset [1] Evaluation and mitigation of soft-errors in neural network based object detection in three GPU architectures 4
  • 5. © 2021 IKERLAN. All rights reserved Failures & Diagnostic Coverage CONTEXT Introduction Object Detection “DC denotes the effectiveness of diagnostic techniques to detect dangerous errors” [1] Diagnostic Coverage (DC) a) Permanent Faults b) Intermittent Faults c) Transient Faults Classification of faults (frequency) 0 1 0 0 0 1 1 0 1 1 1 0 0 0 1 1 0 0 Activation weights to be mapped Activation weights mapped Systematic: associated with the development process Random: Associated to hardware errors. For example: • Electromagnetic interference • Component wear-out • Voltage drops Classification of faults (source) 5 [2] Understanding Error Propagation in Deep Learning Neural Network (DNN) Accelerators and Applications [2] [2] [1] Evaluation Multi-core devices for safety-critical systems: a survey
  • 7. © 2021 IKERLAN. All rights reserved Error avoidance PROPOSED SOLUTION MMM: VIOLATIONS BY RULE OF MISRA C:2012 DARKNET: VIOLATIONS BY RULE OF MISRA C:2012 • Usage of defensive programming • Compliance with codification guidelines (MISRA C [1]) Strategy • Not explicitly defining types with the size and signedness for basic numerical types. • Not checking the correctness of input parameters • Not explicitly defining the desired precedence of operators within expressions • Not explicitly defining input parameter pointers as const-qualified type Violations Sequential MMM 7 [1] MISRA C:2012 – Guidelines for the use of the C language in critical systems • Polyspace Analysis Tool
  • 8. © 2021 IKERLAN. All rights reserved Error Detection Error avoidance To guarantee a safe execution during the software deployment of the Matrix-matrix multiplication (MMM) through the use of diagnostic techniques. Objective a) To employ checksums algorithms as diagnostic techniques to compute an Execution Signature (ES) of all the values of the input and output matrices. b) To provide a catalogue of checksums and to evaluate a trade-off between DC and performance impact. Proposal • XOR • Twos’ complement • Ones’ complement Checksums algorithms[1] • Fletcher • CRC 𝐴11 𝐴12 𝐴21 𝐴22 𝐴31 𝐴32 𝑋 𝐵11 𝐵12 𝐵13 𝐵21 𝐵22 𝐵23 = 𝐶11 𝐶12 𝐶13 𝐶21 𝐶22 𝐶23 𝐶31 𝐶32 𝐶33 PROPOSED SOLUTION [1] The Effectiveness of Checksums for 8
  • 9. © 2021 IKERLAN. All rights reserved Error Detection Error avoidance a) Periodic diagnosis with design time fixed data pattern(s) b) Redundancy (with or without diversity) Safety architectural patterns Control action Inputs (camera/simulation) Switch every N executions ES from MMM including checksums Majority voter MMM including checksums Compare every N executions Diagnostic Known inputs Known ES 9 Voted Output PROPOSED SOLUTION ES from MMM including checksums ES from MMM including checksums
  • 11. © 2021 IKERLAN. All rights reserved EVALUATION Set-up Sequential MMM: • Core R5 implemented in Zynq UltraScale+ AVX-based MMM: • Core i7 implemented in a PC Implementation Square matrices: 80x80, 160x060 and 320x320 Unbalanced matrices: 𝑀 = 18; 𝑁 = 230400; K =64 Performance experiments Square matrices: 20x20, 40x40 and 80x80 Unbalanced matrices: 𝐿1 ∶ 𝑀 = 32; 𝑁 = 29; K = 144 𝐿2 ∶ 𝑀 = 8; 𝑁 = 900; K = 8 𝐿3 ∶ 𝑀 = 15; 𝑁 = 225; K = 48 𝐿59: 𝑀 = 18; 𝑁 = 900; K =1024 DC experiments Unbalanced matrices: Kind of matrices 𝐴11 𝐴12 𝐴21 𝐴22 𝐴31 𝐴32 𝑋 𝐵11 𝐵12 𝐵13 𝐵21 𝐵22 𝐵23 = 𝐶11 𝐶12 𝐶13 𝐶21 𝐶22 𝐶23 𝐶31 𝐶32 𝐶33 𝑀𝑥𝐾 𝑀𝑥𝑁 𝐾𝑥𝑁 Square matrices: Dimension N x N 11
  • 12. © 2021 IKERLAN. All rights reserved Performance Impact (PI) Set-up EVALUATION Performance Impact (PI): 𝑛 = 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒 𝑋 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒𝑌 Definition 𝑋 = 𝐶𝑜𝑑𝑒 𝑎𝑐𝑐𝑜𝑚𝑝𝑙𝑖𝑠ℎ𝑖𝑛𝑔 𝑀𝐼𝑆𝑅𝐴 𝐶 𝑌 = 𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑐𝑜𝑑𝑒 Adoption of MISRA C 𝑋 = 𝑐𝑜𝑑𝑒 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑒𝑑 𝑎𝑛𝑑 𝑎𝑐𝑐𝑜𝑚𝑝𝑙𝑖𝑠ℎ𝑖𝑛𝑔 𝑀𝐼𝑆𝑅𝐴 𝐶 𝑌 = 𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑐𝑜𝑑𝑒 Adoption of MISRA C and code optimization 𝑋 = 𝐶𝑜𝑑𝑒 𝑖𝑛𝑐𝑙𝑢𝑖𝑑𝑖𝑛𝑔 𝑐ℎ𝑒𝑐𝑘𝑠𝑢𝑚/𝑠 𝑌 = 𝐶𝑜𝑑𝑒 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑒𝑑 𝑎𝑛𝑑 𝑎𝑐𝑐𝑜𝑚𝑝𝑙𝑖𝑠ℎ𝑖𝑛𝑔 𝑀𝐼𝑆𝑅𝐴 𝐶 Experiments 𝑛 = 1.01 𝑛 = 0.95 12
  • 13. © 2021 IKERLAN. All rights reserved Performance Impact (PI) Set-up EVALUATION Sequential MMM Remarks 1. Decrement in the PI when the size of the matrices increase, approaching to asymptotically specific values 2. Individual experiments confirm the increase in performance impact from the less intricate algorithms (XOR, 1’s complement, 2’s complement) to even more intricate ones (Fletcher, CRC). 3. The PI is smaller on the most external loops (M,E) with respect to the internal loop (I) 13
  • 14. © 2021 IKERLAN. All rights reserved Performance Impact (PI) Set-up EVALUATION Sequential MMM AVX-based MMM 1. Same tendency to a fixed PI value when the size of the matrices increase. 2. PI Increment in the Fletcher and 1’s complement checksums according to the results obtained in the sequential MMM experiments. Reasons: Lack of specific arithmetic operations in AVX • 1’s complement requires adding all values to be checked and subsequently adding the carry bit back into the result. • Fletcher requires a modulo operation Remarks 14
  • 15. © 2021 IKERLAN. All rights reserved EVALUATION Set-up Performance Impact (PI) Diagnostic Coverage (DC) • Induction of an exhaustive single-bit error injection in all bit positions of the values of matrices A and B 𝐷𝐶 = 𝐷𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝑓𝑎𝑢𝑙𝑡𝑠 𝐼𝑛𝑗𝑒𝑐𝑡𝑒𝑑 𝑓𝑎𝑢𝑙𝑡𝑠 𝑥 100 (%) • DC is independent of the platform where is implemented • The percentage of the code protected is dependent of the loop where the checksum is implemented Fault injection experiment A) External loop injection experiment 𝐴11 𝐴12 𝐴21 𝐴22 𝐴31 𝐴32 𝑋 𝐵11 𝐵12 𝐵13 𝐵21 𝐵22 𝐵23 = 𝐶11 𝐶12 𝐶13 𝐶21 𝐶22 𝐶23 𝐶31 𝐶32 𝐶33 B) Intermediate loop injection experiment 𝐴11 𝐴12 𝐴21 𝐴22 𝐴31 𝐴32 𝑋 𝐵11 𝐵12 𝐵13 𝐵21 𝐵22 𝐵23 = 𝐶11 𝐶12 𝐶13 𝐶21 𝐶22 𝐶23 𝐶31 𝐶32 𝐶33 C) Internal loop injection experiment (100 %) 𝐴11 𝐴12 𝐴21 𝐴22 𝐴31 𝐴32 𝑋 𝐵11 𝐵12 𝐵13 𝐵21 𝐵22 𝐵23 = 𝐶11 𝐶12 𝐶13 𝐶21 𝐶22 𝐶23 𝐶31 𝐶32 𝐶33 15
  • 16. © 2021 IKERLAN. All rights reserved EVALUATION Set-up Performance Impact (PI) Diagnostic Coverage (DC) DC for Sequential MMMs 1. XOR, 1’s and 2’s complements not reach 100% DC in the internal loop. 2. DC is highly dependent on the dimension of the matrix involved in the MMM 3. All the checksums combinations reach 100% DC except for XOR_Fletcher and Two’s_Fletcher 4. Evaluation of a layer extracted from YOLO: 1. 1’ complement -> 98.5 % 2. 2’s complement -> 96.9 % Remarks 16
  • 17. © 2021 IKERLAN. All rights reserved EVALUATION Set-up Performance Impact (PI) Diagnostic Coverage (DC) DC for AVX MMMs 1. DC of XOR (all loops) and 1’s (E), 2’s complement (E), CRC (I) and Fletcher (I) checksums remain the same value. 2. Increment in the DC of the rest of checksum. Reason: AVX instructions compute the ES of 8 data values in each iteration and therefore the percentage of protected data is higher than in the sequential implementation. Remarks DC for Sequential MMMs 17
  • 18. © 2021 IKERLAN. All rights reserved Set-up Performance Impact (PI) Diagnostic Coverage (DC) Trade-off PI vs DC EVALUATION 18 1. All combination of checksums reach 100% DC except for XOR_Fletcher and TWO’s Fletcher 2. Checksums that reach 100% DC: • Fletcher (I) • CRC (I) • XOR_CRC • ONE’s Fletcher • Fletcher_CRC Remarks
  • 19. © 2021 IKERLAN. All rights reserved EVALUATION Set-up Performance Impact (PI) Diagnostic Coverage (DC) Trade-off PI vs DC 19 1. Checksums that reach 100% DC: • Fletcher (I) • CRC (I) • Al combinations of checksums Remarks
  • 21. © 2021 IKERLAN. All rights reserved Conclusions & Future Work 21 Adaptation to coding guidelines Trade-off between DC and PI Future work It doesn’t require a huge effort and the PI is negligible (lower than 1%) • DC evaluation with multiple bit errors instead of single bit-errors • Extend this work towards platform accelerators such as GPUs and FPGAs Achievement of 100% of DC with a PI between n = 1.001 and n=2.97 for the largest matrix sized in the Sequential implementation and n = 1.001 and n= 6.70 in AVX-based implementation High dependence of the performance impact with respect to the dimension of the matrices. The selection of the most appropriate checksums combination depends on the dimensions of the matrix under consideration CONCLUSIONS
  • 22. IKERLAN P.º José María Arizmendiarrieta, 2 - 20500 Arrasate-Mondragón T. +34 943712400 F. +34 943796944 THANK YOU
  • 23. IKERLAN P.º José María Arizmendiarrieta, 2 - 20500 Arrasate-Mondragón T. +34 943712400 F. +34 943796944 NAME: JAVIER FERNÁNDEZ MUÑOZ EMAIL: JAVIER.FERNANDEZ@IKERLAN.ES

Editor's Notes

  1. Ikerlan Technology research centre,
  2. Use of diagnostic techniques in the Matrix-matrix multiplication (MMM) loops to compute an Execution Signature (ES).
  3. Compiler: ARM v8 gcc (without optimization) Compiler: MSVC2018 (without optimization)
  4. 1’s complement require the addition of the data to be protected and subsequently adding the carry bit back into the result. Arithmetic operations with AVX instructions have not carry bit
  5. The reason is that , although the values of A,B,C are summarized with independent checksums, the final ES is obtained by combining these variables and the single bit-errors are masked. Implementing XOR in the internal loop varies from 52.5% DC in unbalanced matrix L1 to only 0.9 % DC in the L2 matrix dimension.