{"id":1064820,"date":"2024-08-02T13:33:36","date_gmt":"2024-08-02T20:33:36","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=1064820"},"modified":"2026-01-05T14:24:26","modified_gmt":"2026-01-05T22:24:26","slug":"aoc","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/aoc\/","title":{"rendered":"AOC"},"content":{"rendered":"
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Analog Optical Computer (AOC)<\/h2>\n\n\n\n

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As industries increasingly rely on AI models and complex optimization, computing demands are soaring\u2014just as digital hardware reaches its limits. To meet this challenge, Microsoft Research has developed the Analog Optical Computer (AOC):<\/strong> the world\u2019s first unconventional computing system capable of accelerating real-world AI inference and optimization workloads.<\/p>\n\n\n\n

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\"Schematic
Schematic of the analog optical computer. In the foreground is the vector-by-matrix multiplication unit. This consists of a 1D array of micro-LEDs, a 2D modulator array (using display projectors), and a 1D array of Silicon sensors.<\/figcaption><\/figure>\n\n\n\n

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At scale the AOC has the potential to solve problems 100x faster or more energy efficiently<\/strong>, than digital systems. This can be achieved by leveraging the parallelism of light and physical processes to perform computations, and avoiding the separation of compute from memory, operating on both continuous and binary data and adopting asynchronous operation.<\/p>\n\n\n\n

Built from low-cost, scalable, and high-volume optical and analog electronics, AOC operates at room temperature. A key innovation of AOC lies in the co-design of hardware and applications, reminiscent of the co-evolution between GPUs and deep learning models.<\/p>\n\n\n\n

To realize its potential, close collaboration on real industry applications is key.<\/p>\n\n\n\n

In partnership with Barclays, we solved a scaled-down version of a high-value financial optimization problem on AOC hardware. Similarly, with the Microsoft Health Futures team, we demonstrated the reconstruction of representative small-scale MRI data, pointing to better patient experiences through faster imaging.<\/p>\n\n\n\n

AOC also runs neural models for image recognition (e.g. MNIST and Fashion MNIST) and nonlinear curve fitting. Beyond what is running on hardware today, we trained a billion-parameter language model on GPUs that applies test-time compute compatible with AOC\u2019s capabilities.<\/p>\n\n\n\n

A quick tour of our AOC lab to highlight the cutting-edge work happening in this space can be viewed, here.<\/p>\n\n\n\n

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