Four years ago a small Microsoft Research team started creating an analog optical computer. They used commercially available parts like sensors from smartphone cameras, optical lenses, and micro-LED lights finer than a human hair. "As the light passes through the sensor at different intensities, the analog optical computer can add and multiply numbers," explains a Microsoft blog post.
They envision the technology scaling to a computer that for certain problems is 100X faster and 100X more energy efficient — running AI workloads "with a fraction of the energy needed and at much greater speed than the GPUs running today's large language models." The results are described in a paper published in the scientific journal Nature, according to the blog post:
At the same time, Microsoft is publicly sharing its "optimization solver" algorithm and the "digital twin" it developed so that researchers from other organizations can investigate this new computing paradigm and propose new problems to solve and new ways to solve them. Francesca Parmigiani, a Microsoft principal research manager who leads the team developing the AOC, explained that the digital twin is a computer-based model that mimics how the real analog optical computer [or "AOC"] behaves; it simulates the same inputs, processes and outputs, but in a digital environment — like a software version of the hardware. This allowed the Microsoft researchers and collaborators to solve optimization problems at a scale that would be useful in real situations. This digital twin will also allow other users to experiment with how problems, either in optimization or in AI, would be mapped and run on the analog optical computer hardware. "To have the kind of success we are dreaming about, we need other researchers to be experimenting and thinking about how this hardware can be used," Parmigiani said.
Hitesh Ballani, who directs research on future AI infrastructure at the Microsoft Research lab in Cambridge, U.K. said he believes the AOC could be a game changer. "We have actually delivered on the hard promise that it can make a big difference in two real-world problems in two domains, banking and healthcare," he said. Further, "we opened up a whole new application domain by showing that exactly the same hardware could serve AI models, too." In the healthcare example described in the Nature paper, the researchers used the digital twin to reconstruct MRI scans with a good degree of accuracy. The research indicates that the device could theoretically cut the time it takes to do those scans from 30 minutes to five. In the banking example, the AOC succeeded in resolving a complex optimization test case with a high degree of accuracy...
As researchers refine the AOC, adding more and more micro-LEDs, it could eventually have millions or even more than a billion weights. At the same time, it should get smaller and smaller as parts are miniaturized, researchers say.
They envision the technology scaling to a computer that for certain problems is 100X faster and 100X more energy efficient — running AI workloads "with a fraction of the energy needed and at much greater speed than the GPUs running today's large language models." The results are described in a paper published in the scientific journal Nature, according to the blog post:
At the same time, Microsoft is publicly sharing its "optimization solver" algorithm and the "digital twin" it developed so that researchers from other organizations can investigate this new computing paradigm and propose new problems to solve and new ways to solve them. Francesca Parmigiani, a Microsoft principal research manager who leads the team developing the AOC, explained that the digital twin is a computer-based model that mimics how the real analog optical computer [or "AOC"] behaves; it simulates the same inputs, processes and outputs, but in a digital environment — like a software version of the hardware. This allowed the Microsoft researchers and collaborators to solve optimization problems at a scale that would be useful in real situations. This digital twin will also allow other users to experiment with how problems, either in optimization or in AI, would be mapped and run on the analog optical computer hardware. "To have the kind of success we are dreaming about, we need other researchers to be experimenting and thinking about how this hardware can be used," Parmigiani said.
Hitesh Ballani, who directs research on future AI infrastructure at the Microsoft Research lab in Cambridge, U.K. said he believes the AOC could be a game changer. "We have actually delivered on the hard promise that it can make a big difference in two real-world problems in two domains, banking and healthcare," he said. Further, "we opened up a whole new application domain by showing that exactly the same hardware could serve AI models, too." In the healthcare example described in the Nature paper, the researchers used the digital twin to reconstruct MRI scans with a good degree of accuracy. The research indicates that the device could theoretically cut the time it takes to do those scans from 30 minutes to five. In the banking example, the AOC succeeded in resolving a complex optimization test case with a high degree of accuracy...
As researchers refine the AOC, adding more and more micro-LEDs, it could eventually have millions or even more than a billion weights. At the same time, it should get smaller and smaller as parts are miniaturized, researchers say.
Read more of this story at Slashdot.
