MEDIA RELATIONS OFFICE
JET PROPULSION LABORATORY
CALIFORNIA INSTITUTE OF TECHNOLOGY
NATIONAL AERONAUTICS AND SPACE ADMINISTRATION
PASADENA, CALIF. 91109 TELEPHONE (818) 354-5011
Contact: John G. Watson
FOR IMMEDIATE RELEASESeptember 10, 1998
JPL NEURAL NETWORK CHIP PAVES THE WAY TO A CLEANER AMERICA AS
FORD SIGNS LICENSING AGREEMENT
A new computer chip that mimics how the human mind works is
making its way from the space program to American industry and
may end up in millions of American cars in years to come.
Computer scientists at NASA's Jet Propulsion Laboratory,
Pasadena, CA, have made advanced neural network technology
breakthroughs that can solve diagnostic problems in industries
from automobiles and aerospace to manufacturing and electricity
JPL and the Ford Motor Co. have signed a licensing agreement
for use of an advanced neural network technology to diagnose
misfiring under the hoods of Ford automobiles, among its many
potential applications. With the advent of this new chip,
vehicles should show a reduction in emission levels.
The smart fit between JPL's neural net hardware and Ford's
automotive engineering algorithm expertise will enhance the
industrial giant's ability to meet ever-stricter Clean Air Act
requirements as they apply to continuous onboard diagnostics and
control, officials said.
In addition, the chip is designed to improve fuel economy,
resulting in financial savings for car owners. Ford engineers do
not predict a price increase for installation of the chip because
JPL designed a computationally powerful neuroprocessor that could
be mass-produced in a highly cost-effective way. The technology
also improves customer satisfaction by virtually eliminating
distracting false alarms about misfiring that vehicle dashboards
can signal with current under-the-hood diagnostic technology.
JPL and Ford scientists say the chip represents the first
significant change in the way computing is done on vehicles since
computers were first introduced into automobiles in the 1970s.
"Neural networks are a new discipline, and diagnostics,
prognostics and control is a huge field. Ford's application is
but the tip of the iceberg of this chip's potential use in
American industry as a whole," said Tom Hamilton, program manager
at JPL's Dual-Use Technology Office, one of JPL's many technology
transfer arms. "JPL is proud to be able to make this
revolutionary technology available for U.S. business."
The licensing agreement comes on the heels of a less formal
Technology Cooperation Agreement that had existed between JPL and
Ford since 1993. Under terms of that agreement, JPL and Ford
engineers worked cooperatively to refine applications of the
emerging technology to Ford's specific needs.
The new license provides Ford with rights to intellectual
property of the chip for auto industry applications, while JPL,
which has applied for patents to the technology, retains general
rights. JPL is managed by the California Institute of
Technology, which serves as the party of record for this license.
Neural systems were inspired by the architecture of nervous
systems of animals, which use neurons, a form of parallel
processing elements, to process large volumes of information
simultaneously. In vehicle applications, artificial neural
networks will "learn" both how to diagnose problems like engine
misfires and control the engine to optimize fuel economy and
"What JPL has brought to the table is expertise in designing
and building what are known as neural network 'application-
specific integrated circuits'," said Dr. Raoul Tawel, who led the
development at JPL for the chip. "With Ford, we are implementing
highly complex neural network software code in dedicated hardware
logic. This brings about a tremendous boost in computational
ability compared to traditional software-based approaches,
enabling real-time onboard diagnostics for the first time."
For misfire diagnostics, it is necessary to observe and
diagnose every engine firing event, estimated at over one billion
in the life of each car.
In addition, the diagnostic error rate has to be extremely
small, less than one in a million, in order to avoid sending
false alarm signals to the driver. The new chip will accomplish
that task by "learning" diagnostic tasks during the vehicle
development process, bypassing the need to develop conventional
software that, in any event, can neither perform these tasks as
well nor be implemented in large production volumes with standard
microprocessors. The neural network chip, designed to carry out
parallel neuron computations efficiently, overcomes the
computational barriers that prevent this technology from being
A detailed, technical explanation of the technology written
by Tawel and Drs. Ken Marko and Lee Feldkamp of Ford's neural
network team, among several others, is available on the Web.
"Custom VLSI ASIC for Automotive Applications with Recurrent
Networks" can be accessed at
For further information about JPL's technology transfer
programs, visit http://techtrans.jpl.nasa.gov/tu.html