A multi-institution research team has developed an optical chip that can train machine learning hardware. Their research is published today in Optica. Machine learning applications have skyrocketed to $165 billion annually, according to a recent report from McKinsey. But before a machine can perform intelligence tasks such as recognizing the details of an image, it must be trained. Training of modern-day artificial intelligence (AI) systems like Tesla's autopilot costs several million dollars in electric power consumption and requires supercomputer-like infrastructure. This surging AI "appetite" leaves an ever-widening gap between computer hardware and demand for AI.

An optical chip has been developed by a multi-institution research group that has the potential to train machine learning hardware. A picture of the chip used for this work. On a yearly basis, machine learning applications skyrocketed to $165B, per a recent McKinsey report. Training for modern-day artificial intelligence (AI) systems similar to Tesla's autopilot costs millions of dollars in electrical power consumption and needs supercomputer-like infrastructure. An ever-widening gap between demand for AI and computer hardware is left by this surging AI "appetite." Photonic integrated circuits, or optical chips, have evolved as a possible solution to provide greater computing performance, as quantified by the operations executed per second per watt utilized (TOPS/W).

Improved computing power and an exponential increase in data have helped fuel the rapid rise of artificial intelligence. But as AI systems become more sophisticated, they'll need even more computational power to address their needs, which traditional computing hardware most likely won't be able to keep up with. To solve the problem, MIT spinout Lightelligence is developing the next generation of computing hardware. The Lightelligence solution makes use of the silicon fabrication platform used for traditional semiconductor chips, but in a novel way. Rather than building chips that use electricity to carry out computations, Lightelligence develops components powered by light that are low energy and fast, and they might just be the hardware we need to power the AI revolution.

The future of Intel is AI. Its books imply as much. The Santa Clara company's AI chip segments notched $1 billion in revenue last year, and Intel expects the market opportunity to grow 30% annually from $2.5 billion in 2017 to $10 billion by 2022. Putting this into perspective, its data-centric revenues now constitute around half of all business across all divisions, up from around a third five years ago. Still, increased competition from the likes of incumbents Nvidia, Qualcomm, Marvell, and AMD; startups like Hailo Technologies, Graphcore, Wave Computing, Esperanto, and Quadric; and even Amazon threaten to slow Intel's gains, which is why the company isn't resting on its laurels.

Inside a small laboratory in Boston's seaport district, buried within a jumble of lasers, lenses, mirrors, and a tangle of wiring, is a tiny chip that might be about to have a big impact on the world of artificial intelligence. The lab belongs to Lightelligence, a startup that's developing a radically new kind of AI accelerator chip. Instead of using electrons to carry out the core mathematical computations needed for machine learning, the company's prototype device uses light. In theory, transferring information at the speed of light means such a device could let AI algorithms run hundreds of times faster than today's best AI chips. Since raw computer power makes such a difference in machine learning, this could mean vastly more powerful and capable algorithms.

Researchers have shown that it is possible to train artificial neural networks directly on an optical chip. The significant breakthrough demonstrates that an optical circuit can perform a critical function of an electronics-based artificial neural network and could lead to less expensive, faster and more energy efficient ways to perform complex tasks such as speech or image recognition. "Using an optical chip to perform neural network computations more efficiently than is possible with digital computers could allow more complex problems to be solved," said research team leader Shanhui Fan of Stanford University. "This would enhance the capability of artificial neural networks to perform tasks required for self-driving cars or to formulate an appropriate response to a spoken question, for example. It could also improve our lives in ways we can't imagine now."

"Using an optical chip to perform neural network computations more efficiently than is possible with digital computers could allow more complex problems to be solved," said research team leader Shanhui Fan of Stanford University. "This would enhance the capability of artificial neural networks to perform tasks required for self-driving cars or to formulate an appropriate response to a spoken question, for example. It could also improve our lives in ways we can't imagine now." An artificial neural network is a type of artificial intelligence that uses connected units to process information in a manner similar to the way the brain processes information. Using these networks to perform a complex task, for instance voice recognition, requires the critical step of training the algorithms to categorize inputs, such as different words.

While there are lots of things that artificial intelligence can't do yet--science being one of them--neural networks are proving themselves increasingly adept at a huge variety of pattern recognition tasks. These tasks can range anywhere from recognizing specific faces in photos to identifying specific patterns of particle decays in physics. Right now, neural networks are typically run on regular computers. Unfortunately, those networks are a poor architectural match; neurons combine both memory and calculations into a single unit, while our computers keep those functions separate. For this reason, some companies are exploring dedicated neural network chips.