Presentation is loading. Please wait.

Presentation is loading. Please wait.

EVOHAR Robustness in Evolved Hardware Jason Masner, John Cavalieri Dr. James Frenzel and Dr. James A. Foster.

Published by Modified over 9 years ago

Similar presentations

Presentation on theme: "EVOHAR Robustness in Evolved Hardware Jason Masner, John Cavalieri Dr. James Frenzel and Dr. James A. Foster."— Presentation transcript:

1 EVOHAR Robustness in Evolved Hardware Jason Masner, John Cavalieri Dr. James Frenzel and Dr. James A. Foster

2 Acknowledgements This work is funded in part through the Center for Secure and Dependable Software Microelectronics Research and Communications Institute DOD/OST BMDO We are grateful for the help and support of these organizations

3 Outline Motivation Sorting Networks Tree Representation Linear Representation Intrinsic Representation Error Introduction Some BS Data Future Work

4 Motivation Study an apparent intrinsic property of evolved systems - ability to degrade gracefully with the presence of local failures. (Robustness for free)

5 Inputs Sorting Network Output 0 1 1 0 0 1 1 0 0 1 0 1 0 1 1 1 1 1 0 0

6 Inputs Sorting Network Output 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1

7 Compare Exchange Circuit AND OR 1 0 Input 0 1 Output

8 CE (1,4) CE (0,4)CE (0,3) CE (1,3)CE (1,2) CE (2,4)CE (0,2) CE (3,4) TREE Structure CE (1,4)CE (0,3)CE (1,3)CE (2,4)CE (0,4) …. ARRAY Structure Individuals

9 Our Xilinx FPGA Cells ….. 64 Inputs 0 1 1 0 0 0 1 1

10 Tree Crossover 1. Two nodes are randomly chosen on each tree. 2. Nodes and subtrees are swapped.

11 Tree Crossover 1. Two nodes are randomly chosen on each tree. 2. Nodes and subtrees are swapped.

12 Tree Crossover 1. Two nodes are randomly chosen on each tree. 2. Nodes and subtrees are swapped.

13 Tree Mutation 1. Two nodes are randomly chosen. 2. Nodes and subtrees are swapped.

14 Tree Mutation 1. Two nodes are randomly chosen. 2. Nodes and subtrees are swapped.

15 Tree Mutation 1. Two nodes are randomly chosen. 2. Nodes and subtrees are swapped.

16 Representation of Individuals CROSSOVER in Arrays CE (1,4)CE (0,3)CE (1,3)CE (2,4)CE (0,4)CE (2,3)CE (1,4)CE (1,2) CE (0,3)CE (3,4)CE (0,2)CE (1,3)CE (2,3)CE (1,2)CE (0,3)CE (0,1)

17 Representation of Individuals CROSSOVER in Arrays CE (1,4)CE (0,3)CE (1,3)CE (2,4)CE (0,4)CE (2,3)CE (1,4)CE (1,2) CE (0,3)CE (3,4)CE (0,2)CE (1,3)CE (2,3)CE (1,2)CE (0,3)CE (0,1) CE (1,4)CE (0,3)CE (2,3)CE (1,4)CE (1,2) CE (0,3)CE (3,4) CE (0,2)CE (1,3)CE (2,3) CE (1,2)CE (0,3)CE (0,1)CE (1,3)CE (2,4)CE (0,4)

18 Representation of Individuals MUTATION in Arrays CE (1,4)CE (0,3)CE (1,3)CE (2,4)CE (0,4)CE (2,3)CE (1,4)CE (1,2)

19 Representation of Individuals MUTATION in Arrays CE (1,4)CE (0,3)CE (1,3)CE (2,4)CE (0,4)CE (2,3)CE (1,4)CE (1,2) CE (1,4)CE (0,3)CE (1,3)CE (2,4)CE (0,4)CE (2,3)CE (1,4)CE (1,2)

20 Errors: Pass-Through AND OR 1 0 Input 0 1 Output 1

21 Error: Stuck on One AND OR 1 0 Input 0 1 Output 1

22 Error: Stuck on Zero AND OR 1 0 Input 0 1 Output 0

23 Error Types and Bit Stability Rating Pass-ThroughStuck on ZeroStuck on One MSN Tree Linear * indicate tendency of a bit to survive uncorrupted through a given network with given set of faults. All errors

24 EVOLUTION BUILDS BETTER CIRCUITS

25 Future Work Testing on different networks Apply the our BS to different data or problems Hillclimber as a control factor Implement in hardware

26 Error Types and Bit Stability Value Pass-ThroughStuck on ZeroStuck on One MSN Tree Linear Numbers indicate tendency of a bit to survive uncorrupted through a given network with given set of faults. All errors

27 Evolutionary Advantages Fast Design Time Less Costly to Develop Generalize Well Evolutionary Circuits are more Robust

Download ppt "EVOHAR Robustness in Evolved Hardware Jason Masner, John Cavalieri Dr. James Frenzel and Dr. James A. Foster."

Similar presentations

1 Denial of Service in Sensor Networks Authors: Anthony D. Wood, John A. Stankovic Presented by: Aiyaz Amin Paniwala.

1 Denial of Service in Sensor Networks Authors: Anthony D. Wood, John A. Stankovic Presented by: Aiyaz Amin Paniwala.
Computer Engineering II

Computer Engineering II
10/14/2005Caltech1 Reliable State Machines Dr. Gary R Burke California Institute of Technology Jet Propulsion Laboratory.

10/14/2005Caltech1 Reliable State Machines Dr. Gary R Burke California Institute of Technology Jet Propulsion Laboratory.
Biologically Inspired AI (mostly GAs). Some Examples of Biologically Inspired Computation Neural networks Evolutionary computation (e.g., genetic algorithms)

Biologically Inspired AI (mostly GAs). Some Examples of Biologically Inspired Computation Neural networks Evolutionary computation (e.g., genetic algorithms)
Genetic Algorithms Sushil J. Louis Evolutionary Computing Systems LAB Dept. of Computer Science University of Nevada, Reno

Genetic Algorithms Sushil J. Louis Evolutionary Computing Systems LAB Dept. of Computer Science University of Nevada, Reno
Institute of Intelligent Power Electronics – IPE Page1 Introduction to Basics of Genetic Algorithms Docent Xiao-Zhi Gao Department of Electrical Engineering.

Institute of Intelligent Power Electronics – IPE Page1 Introduction to Basics of Genetic Algorithms Docent Xiao-Zhi Gao Department of Electrical Engineering.
Energy Aware Self Organized Communication in Complex Networks Jakob Salzmann, Dirk Timmermann SPP 1183 Third Colloquium Organic Computing, 14.-15.09.2006,

Energy Aware Self Organized Communication in Complex Networks Jakob Salzmann, Dirk Timmermann SPP 1183 Third Colloquium Organic Computing, ,
FPGA Design Flow Design Circuit Simulation Implementation Programming.

FPGA Design Flow Design Circuit Simulation Implementation Programming.
Mechanics of Genetic Programming

Mechanics of Genetic Programming
Evolvable by Design Panos Oikonomou James Franck Institute Institute of Biophysical Dynamics The University of Chicago Philippe Cluzel How topology affects.

Evolvable by Design Panos Oikonomou James Franck Institute Institute of Biophysical Dynamics The University of Chicago Philippe Cluzel How topology affects.
1 Evolution in Computation James A. Foster Initiative for Bioinformatics And Evolutionary Studies (IBEST), MRCI, CSDS, and Computer Science University.

1 Evolution in Computation James A. Foster Initiative for Bioinformatics And Evolutionary Studies (IBEST), MRCI, CSDS, and Computer Science University.
A new crossover technique in Genetic Programming Janet Clegg Intelligent Systems Group Electronics Department.

A new crossover technique in Genetic Programming Janet Clegg Intelligent Systems Group Electronics Department.
NEW TIES WP2 Agent and learning mechanisms. Decision making and learning Agents have a controller (decision tree, DQT)  Input: situation (as perceived.

NEW TIES WP2 Agent and learning mechanisms. Decision making and learning Agents have a controller (decision tree, DQT)  Input: situation (as perceived.
Evolving Agents in a Hostile Environment Alex J. Berry.

Evolving Agents in a Hostile Environment Alex J. Berry.
Evolutionary Algorithms Simon M. Lucas. The basic idea Initialise a random population of individuals repeat { evaluate select vary (e.g. mutate or crossover)

Evolutionary Algorithms Simon M. Lucas. The basic idea Initialise a random population of individuals repeat { evaluate select vary (e.g. mutate or crossover)
Presented by Ozgur D. Sahin. Outline Introduction Neighborhood Functions ANF Algorithm Modifications Experimental Results Data Mining using ANF Conclusions.

Presented by Ozgur D. Sahin. Outline Introduction Neighborhood Functions ANF Algorithm Modifications Experimental Results Data Mining using ANF Conclusions.
Evolving Pre-Sort Algorithms Joe Barker. Why Sorting? Sorting is used everywhere Classical methods for developing new algorithms has been pretty much.

Evolving Pre-Sort Algorithms Joe Barker. Why Sorting? Sorting is used everywhere Classical methods for developing new algorithms has been pretty much.
Introduction to Field Programmable Gate Arrays (FPGAs) COE 203 Digital Logic Laboratory Dr. Aiman El-Maleh College of Computer Sciences and Engineering.

Introduction to Field Programmable Gate Arrays (FPGAs) COE 203 Digital Logic Laboratory Dr. Aiman El-Maleh College of Computer Sciences and Engineering.
Evolvable Hardware: Brief introduction to the module Andy Tyrrell Department of Electronics B005 -

Evolvable Hardware: Brief introduction to the module Andy Tyrrell Department of Electronics B005 -
DESIGN OF A SELF- ORGANIZING LEARNING ARRAY SYSTEM Dr. Janusz Starzyk Tsun-Ho Liu Ohio University School of Electrical Engineering and Computer Science.

DESIGN OF A SELF- ORGANIZING LEARNING ARRAY SYSTEM Dr. Janusz Starzyk Tsun-Ho Liu Ohio University School of Electrical Engineering and Computer Science.

Similar presentations

Ads by Google