Edition: U.S. / Global

From Intel, the Thinking Machine's Chip

Published: February 13, 1993

The Intel Corporation has begun shipping samples of a computer chip that imitates the circuitry of the human brain.

The chip is the largest and fastest neural network processor yet built, and computer designers and scientists say the new technology is the most promising approach toward building intelligent machines that mimic hearing, seeing and thinking -- functions today's computers do poorly.

The project is still a gamble for the world's leading maker of the microprocessors that form the core of popular desktop computers. Researchers have been experimenting with systems that simulate "biological computers" for more than a decade. Yet beyond some promising niche applications, Intel has not yet found a widespread use for these chips.

Developed in partnership with Nestor Inc., a Providence, R.I., computer development company, and with only about $1.6 million from the Pentagon, the new chip is designed to recognize patterns of information quickly and precisely. It is structured as an array of 1,024 silicon "neurons," each with a tremendous amount of memory to store and retrieve data.


Artificial neural networks are modeled on the human brain's biological network of hundreds of billions of special cells, or neurons, that transmit information back and forth, making possible behavior like learning and recognition.

The computerized neural networks may one day be able to perform tasks like recognizing objects and understanding speech, which have so far stymied conventional computers. Moreover, with the ability to learn by themselves, such machines would not require the laborious programming of rules and procedures now required to allow computers to work.

"Neural nets offer a whole new way of viewing how computing can be done," Gordon Moore, Intel's chairman and the company's co-founder, said this week. "This represents a significant step toward the mainstream."

The designers of the chip said it was capable of performing 20 billion simple operations a second, a favorable rate when compared with that of today's most powerful supercomputers. Moreover, the new chip, known as the Ni1000, contains 3.7 million transistors, a larger number than Intel's new Pentium personal computer microprocessor, which is yet to be introduced.

In addition, the neural network processor is expected to be more resilient than its conventional microprocessor counterparts, Intel's designers said. Because it is based on a network of separate neurons, a single or even dozens of processing elements can fail and the chip's performance will not be harmed. In contrast, in a conventional microchip, the failure of a single transistor can be fatal.

A number of small start-up companies, as well as large electronics companies like Intel, Motorola, A.T.& T. and Ricoh, are all pursuing neural network systems. But many researchers in the field are skeptical about the economic viability of taking experimental designs and freezing them in silicon chip designs.

"This gives the researchers less freedom to change their particular design," said Ronjon Naj, a neural network expert who is the head of Lexicus Inc., a Palo Alto, Calif., pen software recognition company. "Even if you do use them, the cost of putting them into a machine may limit the application."

But another industry analyst said it was possible that Intel might find a profitable business for its new chip.

"There is a lot of attention being given to fuzzy logic chips in home appliances and cruise controls in cars," said Sanjiv Varma, an artificial intelligence expert at New Science Associates in Mountain View, Calif. "Some entrepreneur might come up with an embedded application for Intel's neural processor that is not high-tech but is more mundane."

Because it is based largely on technology initially developed by Intel's memory division, the neural processor took only a fraction of the resources needed to developed the Pentium. A team of five circuit designers laid out the chip, and it was produced with $1.6 million from the Defense Advanced Research Projects Agency and the Office of Naval Research. Intel invested an additional $1.1 million, and Nestor put up $400,000.

The Pentagon has been investing heavily in smart weapons and a generation of automated battlefield control systems.

A number of researchers said the most interesting feature of Intel's new Ni1000 is the tremendous amount of memory, more than 330 kilobytes on each chip. Memory is increasingly being viewed as more important than raw speed in determining the power of neural networks. Memory provides the context that is thought to be vital to recognition in advanced systems.