The Generic Visual Perception Processor (GVPP) is a chip that mimics the human visual perception system. It can automatically detect and track objects in real-time from a video stream. The GVPP processes visual information as histograms of object locations and velocities. This allows the chip to perform tasks like driving safely, fruit picking, reading and object recognition similarly to the human eye. The GVPP was invented in 1992 and uses a neural network architecture with multiplexing and memory to simulate the work of neurons. It takes weighted sums of inputs and produces outputs to solve problems with minimal programming. The GVPP has applications in automotive, robotics, agriculture, military and other industries involving visual tracking.

1. SEMINAR REPORT ON
GENERIC VISUAL PERCEPTION PROCESSOR
Session :2014-2015
Submitted By:
Deobrat Singh
B.Tech 3rd Year(CSE)
Department Of
Computer Science And Engineering
Madan Mohan Malaviya University of Technology
Gorakhpur-273010

2. INTRODUCTION
 Generic Visual Perception Processor (GVPP) can automatically detect
objects and track their movement in real-time.
 The processor sees its environment as a stream of histograms
regarding the location and velocity of objects.
 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.

3. BACKGROUND OF INVENTION
 Traditional processor failed when there was no predefined algorithm to
solve problem
 Even in these extreme cases GVPP performs well. It can solve problem
with its neural learning function.
 The GVPP was invented in 1992,by BEV founder Patric Pirim.
 Pirim created a chip architecture that mimicked the work of neurons , with
the help of multiplexing and memory.

4. HOW IT WORKS
 There is large number of input lines and an output line to a neuron and
each of them is capable of implementing a simple functions.
 It takes weighted sum of inputs and produces an output that is fed into the
next layer.
 The chip mimics the human eye’s in two processing steps, tonic and
phasic.

5. HARDWARE AND SOFTWARE ASPECTS
SOFTWARE ASPECTS
 A host operating system running on external PC communicates with the
GVPP evaluation board via an OS kernel within the on chip
microprocessor.
 “Programming the GVPP is as simple as setting a few registers and then
testing the results to gauge the application’s success” said Steve Rowe,
BEV’s director of R&D.
 Programming GVPP application takes minimal time.

6. HARDWARE ASPECTS
 The chip houses 23 neural blocks, both temporal and spatial, each consists
of 20 hardware input and output “synaptic” connections.
 GVPP multiplexes this neural hardware with off chip scratchpad memory to
simulate as many as 100,000 synaptic connections per neuron.

7. The retine in the figure is the electronic eye of the system. The
eye referred to a high definition camera.

8. ADVANTAGES
 GVPP has been demonstrated as capable of learning-in-place to solve a
variety of pattern recognition problems.
 It is an inexpensive device that can autonomously "perceive" and then track
up to eight user-specified objects in a video stream
 The engineer needs no knowledge of the internal workings of the GVPP,
the company said, only application-specific domain knowledge.
 The chip could be useful across a wide variety of industries where visual
tracking is important

9.  Simple applications can be quickly prototyped in a few days, with medium-
size applications taking a few weeks and even big applications only a
couple of months

10. DISADVANTAGES
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.

11. APPLICATION
 Automotive Industry
 Robotics
 Agriculture and Fisheries
 Military Applications

12. CONCLUSION
Imitating the human eye's neural networks and the brain, the generic
visual perception processor can handle about 20 billion instructions
per second, and can manage most tasks performed by the eye
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.

13. REFERENCES
 Bart Kosko, Neural networks and fuzzy systems, Addison Wesley
 http://www.techweb.com
 http://www.patentstorm.us
 International Journal of Engineering Research and General
Science Volume 2, Issue 6, October-November, 2014

14. THANK YOU