The 'generic visual perception processor (GVPP)' has been developed after 10 long years of scientific effort . Generic Visual Perception Processor (GVPP) can automatically detect objects and track their movement in real-time . The GVPP, which crunches 20 billion instructions per second (BIPS), models the human perceptual process at the hardware level by mimicking the separate temporal and spatial functions of the eye-to-brain system. The processor sees its environment as a stream of histograms regarding the location and velocity of objects.
GVPP has been demonstrated as capable of learning-in-place to solve a variety of pattern recognition problems. It boasts automatic normalization for varying object size, orientation and lighting conditions, and can function in daylight or darkness.
This electronic "eye" on a chip can now handle most tasks that a normal human eye can. That includes driving safely, selecting ripe fruits, reading and recognizing things. Sadly, though modeled on the visual perception capabilities of the human brain, the chip is not really a medical marvel, poised to cure the blind
The GVPP tracks an "object," defined as a certain set of hue, luminance and saturation values in a specific shape, from frame to frame in a video stream by anticipating where it's leading and trailing edges make "differences" with the background. That means it can track an object through varying light sources or changes in size, as when an object gets closer to the viewer or moves farther away.
The GVPP'S major performance strength over current-day vision systems is its adaptation to varying light conditions. Today's vision systems dictate uniform shadow less illumination ,and even next generation prototype systems, designed to work under "normal" lighting conditions, can be used only dawn to dusk. The GVPP on the other hand, adapt to real time changes in lighting without recalibration, day or light.
For many decades the field of computing has been trapped by the limitations of the traditional processors. Many futuristic technologies have been bound by limitations of these processors .These limitations stemmed from the basic architecture of these processors. Traditional processors work by slicing each and every complex program into simple tasks that a processor could execute. This requires an existence of an algorithm for solution of the particular problem. But there are many situations where there is an inexistence of an algorithm or inability of a human to understand the algorithm.
Even in these extreme cases GVPP performs well. It can solve a problem with its neural learning function. Neural networks are extremely fault tolerant. By their design even if a group of neurons get, the neural network only suffers a smooth degradation of the performance. It won't abruptly fail to work. This is a crucial difference, from traditional processors as they fail to work even if a few components are damaged. GVPP recognizes stores , matches and process patterns. Even if pattern is not recognizable to a human programmer in input the neural network, it will dig it out from the input. Thus GVPP becomes an efficient tool for applications like the pattern matching and recognition
Basically the chip is made of neural network modeled resembling the structure of human brain. The basic element here is a neuron. There are large number of input lines and an output line to a neuron. Each neuron is capable of implementing a simple function. 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.
A large number of such neurons interconnected form a neural network. Every input that is given to the neural network gets transmitted over entire network via direct connections called synaptic connections and feed back paths. Thus the signal ripples in the neural network, every time changing the weighted values associated with each input of every neuron. These changes in the ripples will naturally direct the weights to modify into those values that will become stable .That is, those values does not change. At this point the information about the signal is stored as the weighted values of inputs in the neural network.
A neural network geometrizes computation. When we draw the 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.