11-10-2012, 05:02 PM
GENERIC VISUAL PERCEPTION PROCESSOR
[attachment=36106]
Generic Visual Perception Processor (GVPP)
Models the human perceptual process at the hardware level
by mimicking the separate temporal and spatial functions of the eye-to-brain system
Sees its environment as a stream of histograms regarding the location and velocity of objects
Solve pattern recognition problems
Can function in day light or darkness
Potential sighted
Invented by BEV founder Patric Pirim
A CMOS chip to implement in hardware the separate contributions of temporal and spatial processing in the brain
The brain-eye system uses layers of parallel-processing neurons
Resulting in real-time tracking of multiple moving objects within a visual scene
Major performance strength
Adaptation to varying light sources
-means GVPP adapt to real time changes in lighting without recalibration,day or light
Limitation of traditional processors were removed
-traditional processors slice each and every complex program into simple tasks
-requires an algorithm
GVPP does not require an algorithm
Solve a problem using neural learning function
Fault tolerent
HOW IT WORKS?
The chip is made of neural network modeled resembling the structure of human brain.
The basic element here is a neuron
Each neuron is capable of implementing a simple function
Many input lines and an output line
It takes the weighted sum of its inputs and produces an output that is fed into the next layer
The weights assigned to each input are a variable quantity
NEURAL NETWORK
Geometrizes computation
State diagram of a neural network
The network activity burrows a trajectory in this state space
The trajectory begins with a computation problem
The problem specifies initial conditions which define the beginning of trajectory in the state space
Eg. Pattern learning-patterns to be learned
Eg. Pattern recognition-patterns to be recognized
Trajectory ends when system reaches equilibrium
Final state
[attachment=36106]
Generic Visual Perception Processor (GVPP)
Models the human perceptual process at the hardware level
by mimicking the separate temporal and spatial functions of the eye-to-brain system
Sees its environment as a stream of histograms regarding the location and velocity of objects
Solve pattern recognition problems
Can function in day light or darkness
Potential sighted
Invented by BEV founder Patric Pirim
A CMOS chip to implement in hardware the separate contributions of temporal and spatial processing in the brain
The brain-eye system uses layers of parallel-processing neurons
Resulting in real-time tracking of multiple moving objects within a visual scene
Major performance strength
Adaptation to varying light sources
-means GVPP adapt to real time changes in lighting without recalibration,day or light
Limitation of traditional processors were removed
-traditional processors slice each and every complex program into simple tasks
-requires an algorithm
GVPP does not require an algorithm
Solve a problem using neural learning function
Fault tolerent
HOW IT WORKS?
The chip is made of neural network modeled resembling the structure of human brain.
The basic element here is a neuron
Each neuron is capable of implementing a simple function
Many input lines and an output line
It takes the weighted sum of its inputs and produces an output that is fed into the next layer
The weights assigned to each input are a variable quantity
NEURAL NETWORK
Geometrizes computation
State diagram of a neural network
The network activity burrows a trajectory in this state space
The trajectory begins with a computation problem
The problem specifies initial conditions which define the beginning of trajectory in the state space
Eg. Pattern learning-patterns to be learned
Eg. Pattern recognition-patterns to be recognized
Trajectory ends when system reaches equilibrium
Final state