AI optical computer analyses Covid-19 X-rays
Researchers in Switzerland have developed an optical computer for machine learning that consumes 100 times less power than today’s GPU-based systems.
The team at EPFL in Lausanne has developed an optical computer for machine learning using optical fibres. The Scalable Optical Learning Operator (SOLO) can recognize and classify information formatted as two dimensional images. This was compared to current neural networks on 3,000 X-rays of Covid-19 patients and showed a dramatic lower power consumption.
“Light transmits information without any physical interference from cables. That’s the core advantage of optical technology when it comes to transferring data,” said Demetri Psaltis, head of EPFL’s Optics Laboratory within the School of Engineering. “To take artificial intelligence as an example, many AI programs require accelerators to carry out rapid calculations using minimal power. For now, while optical technology could theoretically meet that need, it has not yet reached the applied stage – despite a half-century of research. That’s because optical computing and decision-making do not yet save either time or energy.”
Designing optical computing devices remains a challenge. Although the computations are performed rapidly , the obstacle comes in transferring the results to memory at that same speed and in an energy-efficient manner.
Engineers at Psaltis’ lab, along with colleges at Christophe Moser’s Laboratory of Applied Photonic Devices, also within the School of Engineering, have developed the SOLO machine learning method, published in Nature Computational Science.
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The machine learning uses the combination of the linear and nonlinear parts of the optical system in a shared volume confined in a multimode fibre (MMF). The principal advantage of this approach is the combination of the three-dimensional connectivity of optics with the long interaction length and lateral confinement in the fibre, using the optical nonlinearities at relatively low optical power. At the same time, the large number of spatial modes that can be densely supported in a MMF maintains the traditional high parallelism feature of optics while maintaining a compact form factor. The availability of megapixel spatial light modulators (SLMs) and cameras means the two-dimensional input and output interfaces to the MMF can sustain a large information processing throughput
“The calculations are executed automatically by the propagation of light pulses inside the fibre. This simplifies the computer’s architecture, retaining only a single neuronal layer, making it a hybrid system,” said Ugur Tegin, the lead co-author of the work.
To test their technology, the team used a dataset consisting of X-ray images of lungs affected by various diseases, including Covid-19. They then ran the data through SOLO to identify the organs affected by the coronavirus. For the purposes of comparison, they also ran the data through a conventional artificial intelligence system with 25 layers of neurons.
“Both systems classified the X-rays equally well. However, our system consumed 100 times less energy,” said Moser. That marked the first time engineers were able to demonstrate quantified power savings. SOLO’s greater energy efficiency could also open the door to new opportunities in other areas of ultra-fast optical computing.
Hybrid optical computing systems are emerging as a promising new technology. “They combine the bandwidth and speed of optical processing with the flexibility of electronic computing. When coupled with artificial intelligence programs in robotics, microscopy and other visual computing tasks, these hybrid systems could achieve some of the transformative capabilities that were, for a long time, imagined as the sole purview of optical computers,” said Psaltis.
The SOLO framework can be realized with silicon-on-insulator technology being used for photonic chips says Moser.
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