Microsoft Research has unveiled an analog optical computer (AOC) with the potential to revolutionize the efficiency of running large language models and tackling complex mathematical problems, stemming from a four-year project within Microsoft’s Future AI Infrastructure initiative.
On September 3rd, the team announced two significant achievements: the development of an optimization-solver algorithm and the creation of an analog digital twin of the AOC. Hitesh Ballani, a researcher in future AI infrastructure at Microsoft Research Cambridge, UK, emphasized the real-world impact of this technology, stating, “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 further noted, “We opened up a whole new application domain by showing that exactly the same hardware could serve AI models, too.”
The AOC project significantly enhances the capabilities of optical data transmission, increasing the number of parameters or weights handled from 64 to 256. The developed optimization-solver algorithm has demonstrated its utility in addressing persistent challenges in the banking sector, with Barclays Bank PLC utilizing it to tackle intricate delivery-versus-payment securities problems. The AOC successfully resolved a transaction settlement scenario involving up to 1,800 hypothetical parties and 28,000 transactions, demonstrating the potential of the technology.
Moreover, the algorithm’s capabilities extend to the medical field, where it can accelerate MRI scans, potentially reducing a 30-minute process to five minutes. Michael Hansen, senior director of biomedical signal processing at Microsoft Health Futures, clarified, “To be transparent, it’s not something we can go and use clinically right now… but it gives you that little spark that says, ‘Oh boy! If this instrument was actually in full scale’…” Microsoft Health Futures employed a digital twin of the AOC to validate the machine’s feasibility and scale its capabilities.
Francesca Parmigiani, a Microsoft principal research manager and the head of the AOC development team, characterized the computer as specialized but highly effective, stating that it is “not a general-purpose computer, but what we believe is that we can find a wide range of applications and real-world problems where the computer can be extremely successful.” A significant advantage of the AOC lies in its potential to reduce the energy consumption associated with large language model (LLM) reasoning by employing a fixed-point search computation method.
The AOC operates by performing computations based on varying light intensities that pass through a digital sensor, with the researchers constructing projectors using optical lenses, digital sensors, and micro-LEDs, and prioritizing commercially available components to minimize costs. Microsoft Research plans to develop a new generation of the AOC every two years, with Parmigiani emphasizing the need for other researchers to experiment and think about how this hardware can be used.
The team’s findings have been published in the journal *Nature*, and a repository for the digital twin of the AOC is available on GitHub, encouraging further research and development in this field.
