Optical Computing: The Future of AI Efficiency and Performance
Moore’s law, which states that the number of transistors on a computer chip doubles approximately every two years, is being outpaced by the rapid growth of computing demands, particularly in the era of deep learning. The International Energy Agency forecasts that by 2026, AI will consume ten times more power than it did in 2023, with data centers using as much energy as Japan. According to Nick Harris, CEO of Lightmatter, AI’s computational power needs double every three months, far exceeding Moore’s law, posing significant economic challenges.
A promising solution lies in optical computing, which processes information using photons instead of electrons. Optical signals can carry more information, operate at higher frequencies, and offer greater efficiency. This makes optical computing potentially more suitable for AI, which demands high-speed and efficient processing. Optical computers could run more operations simultaneously while consuming less energy.
Optical computing’s potential has long been recognized, with early neural networks in the 1980s and 1990s utilizing optical systems. Recent advancements have demonstrated optical computing’s advantages in matrix multiplication, a fundamental operation in neural networks. In 2017, MIT researchers created an optical neural network on a silicon chip, achieving faster and more efficient performance in tasks like speech recognition.
Progress continues with innovations such as HITOP, an optical network developed by MIT and USC researchers, which scales computation throughput by utilizing multiple dimensions of light. Although still behind electronic systems in raw power, HITOP significantly reduces energy costs per calculation.
Specialized applications may see optical AI systems succeed first. For instance, an optical neural network developed by Queen’s University can quickly and efficiently sort through wireless transmissions, outperforming electronic systems in speed and power consumption. While the full potential of optical computing is yet to be realized, ongoing research suggests that it could revolutionize AI by significantly enhancing efficiency and performance.
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