Think of what you could do with this as a $6.00 part!

French firm offers $6 vision system-on-a-chip
By R. Colin Johnson
EE Times
(03/09/00, 09:45:27 AM EDT)

FORT LAUDERDALE, Fla. ( ChipWire) -- A French technology research company
has unwrapped a vision-processing system-on-a-chip that it claims can be
manufactured as inexpensively as a microcontroller. The $6 Generic Visual
Perception Processor (GVPP) can automatically detect objects and track their
movement in real-time, according to Bureau d'Etudes Vision (BEV).

The rights to manufacture the GVPP will be up for grabs at a technology
auction slated for next month. If properly commercialized, auctioneers
PriceWaterhouseCoopers LLP estimate a multibillion-dollar gross revenue
stream for the GVPP based on 100 proposed applications in 10 industries.

"We couldn't manage multiple licenses to competing companies," said Nabeel
Al-Adsani, director of operations at BEV. "Instead, we hope to interest
major semiconductor manufacturers in licensing the GVPP so that they can
supply the application-specific companies with chips."

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. Those objects could be the white lines on a highway,
the footballin a televised game or the annotated movement of enemy ground
forces from satellite telemetry.

Alongside a CMOS imager on its 2-x-4-inch evaluation board, the 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.

A complete GVPP system including the charge-coupled device and all support
circuitry should cost less than $50, the company said. BEV also claimed that
the software it provides with the chips permits engineers to develop
applications for the GVPP in just a few weeks.

The GVPP was invented in 1992, when BEV founder Patric Pirim saw that it
would be relatively simple for 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 that pass the
signal through a series of preprocessing steps, resulting in real-time
tracking of multiple moving objects within a visual scene.

Pirim created a chip architecture that mimicked the work of the neurons,
with the help of multiplexing and memory. The result is an inexpensive
device that can autonomously "perceive" and then track up to eight
user-specified objects in a video stream based on hue, luminance,
saturation, spatial orientation, speed and direction of motion, the company
claims .

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 its 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 chip houses 23 neural blocks, both temporal and spatial, each consisting
of 20 hardware input and output "synaptic" connections. The GVPP multiplexes
this neural hardware with off-chip scratchpad memory to simulate as many as
100,000 synaptic connections per neuron. Each of these synapses can be
changed through the on-chip microprocessor for a combined processing total
of over 6.2 billion synaptic connections per second.

In executing up to 20 bips to analyze successive frames of a video stream,
the temporal neurons identify pixels that have changed over time and
generate a 3-bit value indicative of the magnitude of that change. The
spatial-processing system analyzes the resulting "difference" histogram to
calculate the speed and direction of the motion.

The GVPP's major performance strength over current-day $10,000 vision
systems is its automatic adaptation to varying lighting conditions. Today's
vision systems dictate uniform, shadowless illumination, and even
next-generation prototype systems, designed to work under "normal" lighting
conditions, can be used only from dawn to dusk. The GVPP, on the other hand,
adapts to real-time changes in lighting without recalibration, day or night.

Since processing in each module on the GVPP runs in parallel out of its own
memory space, multiple GVPP chips can be cascaded to expand the number of
objects that can be recognized and tracked. When set in master-slave mode,
any number of GVPP chips can divide and conquer, for instance, complex
stereoscopic vision applications.

On the software side, a host operating system running on an external PC
communicates with the GVPP's evaluation board via an OS kernel within the
on-chip microprocessor. BEV dubs the neural-learning capability of its
development environment "programming by seeing and doing," because of its
ease of use. The engineer needs no knowledge of the internal workings of the
GVPP, the company said, only application-specific domain knowledge.

"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 research and development. "Once debugged, these tiny
application programs are loaded directly into the GVPP's internal ROM."

Application programs themselves can use C++, which makes calls to a library
of assembly language algorithms for visual perception and tracking of
objects. The system's modular approach permits the developer to create a
hierarchy of application building blocks that simplify problems with
inheritable software characteristics.

"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," said Rowe.

In applications, each pixel may be described with respect to any of the six
domains of information available to it: hue, luminance, saturation, speed,
direction of motion and spatial orientation. The GVPP further subcategorizes
pixels by ranges, for instance luminance within 10% and 65%, hue of blue,
saturation between 20% and 25% and moving upward in scene.

A set of second-level pattern recognition commands permits the GVPP to
search for different objects in different parts of the scene -- for
instance, to look for a closed eyelid only within the rectangle bordered by
the corners of the eye. Since some applications may also require multiple
levels of recognition, the GVPP has software hooks to pass along the
recognition task from level to level.

For instance, to detect when a driver is falling asleep -- a capability that
could find use in California, which is about to mandate that cars sound an
"alarm" when drowsy drivers begin to nod off --the GVPP is first programmed
to detect the driver's head, for which it creates histograms of head
movement. The microprocessor reads these histograms to identify the area for
the eye.

Then the recognition task passes to the next level, which searches only
within the eye area rectangles. High-speed movement there, normally
indicative of blinking, is discounted, but when blinks become slower than a
predetermined level, they are interpreted as the driver nodding off, and
trigger an alarm.

Pirim has long-term plans out to 2006 for the GVPP. "We have a very clear
set of upgrades to take advantage of putting more transistors onto our
system-on-a-chip," said Pirim.

First, a CMOS imager will be integrated on-chip with the GVPP, enabling
watch-size vision systems by 2002. After that, Pirim plans to integrate
flash memory that will enable a system the size of a pinkie ring by 2004.
And by 2006, Pirim has slated an expanded on-chip DRAM plus beefed up
on-chip processing to solve multisensor fusion applications in hat-pin-size
vision systems.

Application-specific software libraries are also planned, including optical
character recognition, 3-D analysis and spatial organization.

BEV lists possible applications for the GVPP in process monitoring, quality
control and assembly; automotive systems such as intelligent air bags that
monitor passenger size and traffic congestion monitors; pedestrian
detection, license plate recognition, electronic toll collection, automatic
parking management, automatic inspection; and medical uses including disease
identification. The chip could also prove useful in unmanned air vehicles,
miniature smart weapons, ground reconnaissance and other military
applications, as well as in security access using facial, iris, fingerprint,
or height and gait identification.


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