This light-based engineer’s computers take inspiration from the brain

Bhavin Shastri still gets excited when he sees a laser pointer and has been fascinated by them since he was about 10 years old. “I was amazed that a beam of light could retain its brightness, concentrated in a small spot even after traveling a great distance,” says Shastri. “A laser pointer in my hand felt like a lightsaber Star Wars.

Now a physicist and engineer at Queen’s University in Kingston, Canada, Shastri wants to create light- or photonic-based computers. And he wants them to mimic the human brain.

Standard computers rely on electricity, using wires to transmit data via electrical currents. Photonic computers rely on light in the form of laser beams. Filters along the way change the intensity of the light to perform the calculations.

Although researchers have used light to transmit, store and process data in the laboratory, photonic computing is still in its infancy. Shastri is determined to push those boundaries. Its photonic computer chips are packaged together and connect photonic components that behave like brain neurons, creating a physical neural network on a chip. “Physics mimics biology,” says Shastri.

These types of chips are more powerful for certain applications and can be a big help for AI.

Modeling computers as brains

Shastri’s interest in light began young. He recalls an experiment he saw as a child: A plastic water bottle was punctured near its base so that a steady stream of water flowed out and down under the force of gravity. A laser shone through the hole in the bottle and, to Shastri’s surprise, it did not continue on a horizontal path. Instead, the beam bent down with the flow of water. “I was completely blown away by this experiment,” he says.

Since then, Shastri has been thinking about how light can be manipulated, while also exploring other research interests. In college, he worked with a professor who was researching machine learning and artificial intelligence, which sparked a new passion. Later, as a postdoc at Princeton University, Shastri met optical physicist Paul Prucnal, who would serve as Shastri’s advisor.

Prucnal told Shastri about his research creating “a laser that behaves like a biological neuron,” Shastri says, and how the team was looking to use such a laser to compute with light. This idea caught Shastri’s attention.

Shastri was “the first to connect the dots,” Prucnal says, when he realized that photonics could overcome some serious limitations of electronics.

Standard computers are “reaching their fundamental limits,” says Shastri. When most modern computers do computations, they cannot access much of their memory, and when they retrieve information from memory, they cannot compute. This makes computers slow and difficult for AI, image processing and other computations with high processing demands. Training and running today’s AI algorithms consumes a huge amount of energy — collectively, it’s predicted to require as much as Japan’s total electricity consumption by 2026. Computers with brain-mimicking architectures, or neuromorphic computers , promise to be faster and use less energy.

“We want to build machines that will be much more energy efficient and much faster than other computers,” says Shastri.

But assembling enough wires on a chip to form a brain-like network of connections for use in an electronic computer is not easy. Electrical currents in close proximity will exert unwanted magnetic forces on each other, resulting in overheating and erratic performance. Light, however, usually does not interact with other light. Thus, countless light rays of different wavelengths can pass through the same path at the same time without any problem.

Prucnal notes that Shastri was the first to successfully create neuromorphic photonic computers on a chip. “Bhavin pioneered a way of thinking,” he says.

The study of light

A self-described “strong experimentalist,” Shastri designs, engineers, builds, and conducts experiments on chip-sized photonic devices. His team began by studying simpler devices similar to a single neuron, analyzing how they could mimic the function of a biological neuron. Years later, in as-yet-unpublished work, researchers have tentatively demonstrated that a chip with 100,000 neuron-like components can perform 120 billion operations per second, Shastri says—about 40 times faster than an average electronic computer.

Daniel Brunner, a machine learning and computing researcher at the FEMTO-ST Institute in France, who met Shastri when they were both postdocs, praised Shastri’s groundbreaking work. “I can’t even count the publications where he laid the groundwork” for using photonics to create physical neural networks, Brunner says.

And Shastri’s brilliance goes beyond his “incredible energy” and “incredible ability,” Prucnal says: Shastri is able to bring people together. “It’s not just about being likable, it’s about having a vision of how to do it [unite] these different fields”, he adds.

Don’t expect a photonic neuromorphic computer in your home anytime soon, though. These computers are best suited for specific research or industry applications. In addition to AI, Shastri and his colleagues are working on applications involving old problems in radio signal optimization and image processing.

Shastri may be committed to transforming computers, but his work is motivated by a decades-long fascination with light and its properties. “I’ve been very lucky to be able to do something,” he says, “it’s always been my childhood dream.”