BUILDING AN ARTIFICIAL
BRAIN
Using an FPGA CAM-Brain Machine
Mika Shoshani
Yossy Salpeter
An ARTIFICIAL BRAIN?!
• What?
– A machine modeling the Human brain
• Why?
– Breaking the limits of traditional computers
• And How?
– “Teaching” the machine…
Scope
• Introduction
• Background
– The basis of the “Brain Building” field
• The CAM-Brain machine
• Domo Arigato Mr. ROBOKONEKO
– “Proof of concept”
• What’s Next...
Buzz words
•
•
•
•
•
•
Neurons, Axons, Dendrites…
Neural Network Module
CAM - Cellular Automata Model
FPGA - Field Programmable Gate Array
Genetic Algorithms
“Evolvable Hardware”
The Human Brain
• A network of 1014 neurons
• Data transfer by electric signals
• Dendrite cells (neurons Input)
– Collect signals and pass them to the neuron
• Neurons
– “Decide” when to initiate a signal
• Axon cells (neurons Output)
– Propagate neuron signals
Genetic Algorithms
• A process imitating natural evolution
Random population
Fitness function
REPRODUCTION
New Generation
Crossover & Mutation
The fittest
Genetic Algorithms
• A process imitating natural evolution
Random population
Fitness function
REPRODUCTION
3’ed Generation
Crossover & Mutation
The fittest
Genetic Algorithms
• A process imitating natural evolution
Random population
Fitness function
REPRODUCTION
4’th Generation
Crossover & Mutation
The fittest
Genetic Algorithms
• A process imitating natural evolution
Random population
Fittest individual
Fitness function
REPRODUCTION
5’th Generation
Crossover & Mutation
The fittest
“Evolvable Hardware”
• The Application of a Genetic Algorithm
on programmable hardware:
Chip with
random circuits
Measuring circuit
Functioning
circuit
REPRODUCTION
New Generation
of mutant circuits
Random Mutations
Best
Performing
circuits
Evolve Hardware to perform a desired function
Human Brain vs. The
Computer
•
•
•
•
1014 Neurons
Parallel Computing
Speed: 100+ M./sec.
Natural Evolution
•
•
•
•
CPU - Central Processing Unit
Serial Computing
Approx. Speed of light
“Designable”
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
CBM Goal
• Create a complex functionality without
any a priori knowledge of how to
achieve it…
• Requires the desired Input/Output function!
CELLULAR automata
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.
CBM Neural Network Model
• The CBM implements the:
“CoDi” Cellular Automata based
neural network model
• Goals:
– Fast evolution
– Portability into electronic hardware
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
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
CoDI Module Evolving
CoDI Module evolution
• 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
CBM Architecture
•
•
•
•
•
•
Cellular Automata Module
Genotype/Phenotype Memory
Fitness Evaluation Unit
Genetic Algorithm Unit
Module Interconnection Memory
External Interface
Architecture {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
Architecture {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
Architecture {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
Architecture {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
Architecture {5}
• Module Interconnection Memory
– Supports operation of Evolved modules as
one artificial brain
– Provides signaling between modules
Architecture {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
Human Brain
•
•
•
•
1014 Neurons
Parallel Computing
Speed: 100+ M./sec.
Natural Evolution
vs.
•
•
•
•
CAM-Brain
4*107 Neurons
1150 parallel neurons
Approx. speed of light
“Designable” Evolution
ROBOKONEKO
•
•
•
•
Political & Strategic goals
A controlled cat as a “proof of concept”
Radio connected to CBM
Demonstrates CBM via evolved
behaviors
• Goal - The “CUTE” factor...
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
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
What’s Next...
• “Intelligent” robotic pets, Household
robots, Soldier robots.
• Artilect - Artificial Intellect
• Ultra-Intelligent Artilect = Moral dilemma
The prophecy
• Future WAR “Cosmists” vs. “Terrans”…
• The End of Human race as we know it...
References {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)
References {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- ‫האוניברסיטה העברית בירושלים‬, http://gifted.snunit.k12.il/activities/brain/