The document discusses building an artificial brain using an FPGA-based CAM-Brain machine. The CAM-Brain project aims to build an artificial brain with 32,000 evolved neural network modules and 40 million neurons. As a proof of concept, the CAM-Brain is controlling a robot cat named Robokoneko through evolved motion control modules. The ultimate goals are intelligent robotic pets and household robots.

1. BUILDING AN ARTIFICIALBUILDING AN ARTIFICIAL
BRAINBRAIN
Using an FPGA CAM-Brain MachineUsing an FPGA CAM-Brain Machine
Mika ShoshaniMika Shoshani
Yossy SalpeterYossy Salpeter

2. An ARTI FI CI AL BRAI N?!
• What?
– A machine modeling the Human brain
• Why?
– Breaking the limits of traditional computers
• And How?
– “Teaching” the machine…

3. Scope
• Introduction
• Background
– The basis of the “Brain Building” field
• The CAM-Brain machine
• Domo Arigato Mr. ROBOKONEKOROBOKONEKO
– “Proof of concept”
• What’s Next...

4. Buzz words
• Neurons, Axons, Dendrites…
• Neural Network Module
• CAM - Cellular Automata Model
• FPGA - Field Programmable Gate Array
• Genetic Algorithms
• “Evolvable Hardware”

5. • A network of 1014
neurons
• Data transfer by electric signals
• DendriteDendrite cells (neurons Input)
– Collect signals and pass them to the neuron
• NeuronsNeurons
– “Decide” when to initiate a signal
• AxonAxon cells (neurons Output)
– Propagate neuron signals
The Human Brain

6. Genet ic Algorit hms
• A process imitating natural evolution
Random population
Fitness function
The fittestCrossover & MutationNew Generation
REPRODUCTION

7. Genet ic Algorit hms
• A process imitating natural evolution
Random population
Fitness function
The fittestCrossover & Mutation3’ed Generation
REPRODUCTION

8. Genet ic Algorit hms
• A process imitating natural evolution
Random population
Fitness function
The fittestCrossover & Mutation4’th Generation
REPRODUCTION

9. Genet ic Algorit hms
• A process imitating natural evolution
Random population
Fitness function
The fittestCrossover & Mutation5’th Generation
Fittest individual
REPRODUCTION

10. “Evolvable Hardware”
• The Application of a Genetic Algorithm
on programmable hardware:
Chip with
random circuits
Measuring circuit
Best
Performing
circuits
Random MutationsNew Generation
of mutant circuits
REPRODUCTION
Functioning
circuit
Evolve Hardware to perform a desired functionEvolve Hardware to perform a desired function
AT
HARDWARE
SPEEDS!!!

11. Human Brain vs. The
Comput er
• 1014
Neurons
• Parallel Computing
• Speed: 100+ M./sec.
• Natural Evolution
• CPU - Central Processing Unit
• Serial Computing
• Approx. Speed of light
• “Designable”

12. The CAM-Brain Machine
(CBM)
• A research tool of an artificial brain
• Consists of 32,768 neural modules
• Neural modules evolve in hardware
using Genetic Algorithms

13. CBM Goal
• Create a complex functionality without any a priori
knowledge of howhow to achieve it…
• Requires the desired Input/Output function!

14. CELLULAR aut omat a
MODEL
• A 3D grid of cells
• Each can be in one of a finite number of
possible states.
• Sync. updated in discrete time steps.
• According to a local, identical interaction
rule. “Chromosome”

15. CBM Neural Net work Model
• The CBM implements the:
“CoDi” Cellular Automata based
neural network model
• Goals:
– Fast evolution
– Portability into electronic hardware

16. CoDI Cell design
• A cube with six neighbor cells
• Can function as Neuron, Axon or Dendrite
• A Neuron Cell:
– 5 dendritic inputs + 1 axonic output
– 4-bit input accumulator, “fires” on threshold
• A Dendrite cell: 5 Inputs / 1 Output
• An Axon cell: 1 Input / 5 Outputs

17. CoDI Module Evolving
• All cells are seeded with “chromosome”
• Seed Neuron cells randomly
• Growth procedure:
– Each Neuron sends grow dendrite/axon signals
– Blank cells become dendrite/axon
– Grown cells propagate growth signals
– Propagation direction is set by the chromosome

18. CoDI Module Evolving

19. CoDI Module evolut ion
• Each module is given a specific function
• Genetic Algorithem:
– Initial population of 30-100 modules
– Run for 200-600 Generations
– Up to 60,000 different module evaluations
• Full module evolution takes approx. 1sec

20. CBM Archit ect ure
• Cellular Automata Module
• Genotype/Phenotype Memory
• Fitness Evaluation Unit
• Genetic Algorithm Unit
• Module Interconnection Memory
• External Interface

21. Archit ect ure {1}
• Cellular Automata Module
– The hardware core of the CBM
– 3D array of identical logic circuits (cells)
– Module size of 24*24*24 cells (13,824)
– Implemented by 72 FGPAs
– Time shared between multiple modules -
Forming a brain during simulation.
– No idle time between modules

22. Archit ect ure {2}
• Genotype & Phenotype Memory
– Total 1180 Mbytes RAM
– Genotype memory for Evolution mode:
• Store Chromosome bitstrings
• Store module neuron location & orientation
– Phenotype memory for Run mode:
• Holds all evolved module maps
– Can support up to 32,758 modules

23. Archit ect ure {3}
• Fitness evaluation unit
– Evaluates module fitness
– Signals each module inputs
– Compares Module output to target output
– This comparison gives a measure of
module performance

24. Archit ect ure {4}
• Genetic Algorithm Unit
– Selects a subset of the “best” evolved
modules for reproduction
– Implements Crossover and Mutation masks
– Generates offspring modules
– Offspring chromosome generated in
hardware

25. Archit ect ure {5}
• Module Interconnection Memory
– Supports operation of Evolved modules as
one artificial brain
– Provides signaling between modules

26. Archit ect ure {6}
• External Interface
– CBM Signaling is by 1-bit spiketrains
– I/O For each module
• Input of up to 188 spiketrains
• Output of up to 3 spiketrains

27. Human Brain vs. CAM-Brain
• 1014
Neurons
• Parallel Computing
• Speed: 100+ M./sec.
• Natural Evolution
• 4*107
Neurons
• 1150 parallel neurons
• Approx. speed of light
• “Designable” Evolution

28. • Political & Strategic goals
• A controlled cat as a “proof of concept”
• Radio connected to CBM
• Demonstrates CBM via evolved
behaviors
• GoalGoal - The “CUTE” factor...
ROBOKONEKO

29. Behavior Evolving
• Moition control modules
– Fitness criterion - speed & distance
– Mechanical vs. Simulated behavior evolving
– Slow evolution, 2-3 min. per chromosome
– Hand coded base criterion.
• Non motion control modules evolution
-Predicted to be Faster

30. SUMMARY
• Artificial Brain Building
• “CAM Brain Project”
– Aims to build an artificial brain with 32000
evolved net modules, 40 million neurons
• “Robokoneko”
– A Cat robot controled by the CAM-Brain
– In development of motion control modules

31. What ’s Next ...
• “Intelligent” robotic pets, Household
robots, Soldier robots.
• Artilect - Artificial Intellect
• Ultra-Intelligent Artilect = Moral dilemma

32. The prophecy
• Future WAR “Cosmists” vs. “Terrans”…
• The End of Human race as we know it...

33. Ref erences {1}
• "Building an Artificial Brain Using an FPGA Based CAM-Brain
Machine", Applied Mathematics and Computation Journal, Special Issue on
"Artificial Life and Robotics, Artificial Brain, Brain Computing and
Brainware", North Holland. (Invited by Editor, to appear 1999), Hugo de
Garis, Michael Korkin, Felix Gers, Eiji Nawa, Michael Hough.
• "A 40 Million Neuron Artificial Brain for an Adaptive Robot Kitten
"Robokoneko", Hugo de Garis, Michael Korkin, Gary Fehr, Nikolai Petroff,
Eiji Nawa, to be submitted to the Connection Science Journal, Special Issue on
Adaptive Robots.
• "Simulation and Evolution of the Motions of a Life Sized Kitten Robot
"Robokoneko" as Controlled by a 32000 Neural Net Module Artificial
Brain", Hugo de Garis, Nikolai Petroff, Michael Korkin, Gary Fehr, Eiji
Nawa, (Invitation by Editor to the Computational Geometry Journal (CGJ),
Special Issue on Computational Geometry in Virtual Reality)

34. Ref erences {www}
• A Brief Introduction to Genetic Algorithms, by Moshe Sipper,
http://lslsun.epfl.ch/~moshes/ga_main.html
• Non-uniform cellular automata, by Moshe Sipper,
http://lslsun.epfl.ch/~moshes/ga_main.html
• Prof. Dr. Hugo de Garis Home Page, http://www.cs.usu.edu/~degaris/
• CNN - Swiss scientists warn of robot Armageddon,
http://www.cnn.com/TECH/science/9802/18/swiss.robot/
• ‫המוח‬ - ‫בירושלים‬ ‫העברית‬ ‫,האוניברסיטה‬ http://gifted.snunit.k12.il/activities/brain/