Twenty years ago, a Duke University professor, David R. Smith, used artificial composite materials called “metamaterials” to make a real-life invisibility cloak. While this cloak didn’t really work like Harry Potter’s, exhibiting limited ability to conceal objects from the light of a single microwave length, those advances in material science did eventually trickle down to electromagnetism research.
Today, Austin-based Neurophos, a photonics startup spun out of Duke University and Metacept (an incubator run by Smith), is taking that research further to solve what may be the biggest problem facing AI labs and hyperscalers: how to scale computing power while keeping power consumption in check.
The startup has come up with a “metasurface modulator” with optical properties that enable it to serve as a tensor core processor for doing matrix vector multiplication — math that is at the heart of a lot of AI work (particularly inferencing), currently performed by specialized GPUs and TPUs that use traditional silicon gates and transistors. By fitting thousands of these modulators on a chip, Neurophos claims, its “optical processing unit” is significantly faster than the silicon GPUs currently used en masse at AI data centers, and far more efficient at inferencing (running trained models), which can be a fairly expensive task.
To fund the development of its chips, Neurophos has just raised $110 million in a Series A round led by Gates Frontier (Bill Gates’ venture firm), with participation from Microsoft’s M12, Carbon Direct, Aramco Ventures, Bosch Ventures, Tectonic Ventures, Space Capital, and others.
Now, photonic chips are nothing new. In theory, photonic chips offer higher performance than traditional silicon because light produces less heat than electricity, it can travel faster, and is far less susceptible to changes in temperature and electromagnetic fields.
But optical components tend to be much larger than their silicon counterparts, and can be difficult to mass-produce. And photonic chips also need converters to transform data from digital to analog and back, which can be large and take up a lot of power.
Neurophos, however, posits that the metasurface it has developed can …