AI at the speed of light just became a possibility

'Our method performs the same kinds of operations that today's GPUs handle, like convolutions and attention layers, but does them all at the speed of light,' says Dr. Zhang. 'Instead of relying on electronic circuits, we use the physical properties of light to perform many computations simultaneously.'

To achieve this, the researchers encoded digital data into the amplitude and phase of light waves, effectively turning numbers into physical properties of the optical field. When these light fields interact and combine, they naturally carry out mathematical operations such as matrix and tensor multiplications, which form the core of deep learning algorithms. By introducing multiple wavelengths of light, the team extended this approach to handle even higher-order tensor operations.

'Imagine you're a customs officer who must inspect every parcel through multiple machines with different functions and then sort them into the right bins,' Zhang explains. 'Normally, you'd process each parcel one by one. Our optical computing method merges all parcels and all machines together - we create multiple 'optical hooks' that connect each input to its correct output. With just one operation, one pass of light, all inspections and sorting happen instantly and in parallel.'

Another key advantage of this method is its simplicity. The optical operations occur passively as the light propagates, so no active control or electronic switching is needed during computation.

'This approach can be implemented on almost any optical platform,' says Professor Zhipei Sun, leader of Aalto University's Photonics Group. 'In the future, we plan to integrate this computational framework directly onto photonic chips, enabling light-based processors to perform complex AI tasks with extremely low power consumption.'