Core Insights - The article discusses the rapid development of scientific research and industrial applications driven by artificial intelligence (AI) and optimization, while highlighting the significant energy consumption challenges these technologies pose for sustainable digital computing [1][2]. Group 1: Analog Optical Computer (AOC) - The Microsoft Cambridge Research team proposed the Analog Optical Computer (AOC), which can efficiently perform AI inference and optimization tasks without frequent digital conversions, offering significant scalability and energy efficiency advantages [3][5]. - AOC combines analog electronic technology with 3D optical technology, enabling a dual-domain capability that enhances noise resistance and supports recursive reasoning in computationally intensive neural models [5][7]. - The AOC architecture is built on scalable consumer-grade technology, providing a promising path for faster and more sustainable computing [7][18]. Group 2: Applications and Performance - AOC is primarily aimed at two types of tasks: machine learning inference and combinatorial optimization, with the research team demonstrating its capabilities through four typical case studies [8]. - In machine learning tasks, AOC successfully executed image classification and nonlinear regression, achieving higher accuracy compared to traditional linear classifiers [9]. - For combinatorial optimization, AOC demonstrated its effectiveness in medical image reconstruction and financial transaction settlement, achieving accurate results without any digital post-processing [10][11]. Group 3: Scalability and Efficiency - AOC is expected to support models with parameter scales ranging from 100 million to 2 billion, requiring between 50 to 1000 optical modules for operation [16][17]. - The estimated power consumption for processing a matrix with 100 million weights using 25 AOC modules is 800 W, achieving a computational speed of 400 Peta-OPS, with energy efficiency of 500 TOPS per watt [17]. - AOC's architecture shows potential for achieving approximately 100 times energy efficiency improvement in practical machine learning and optimization tasks [18][19].