为了支持现代人工通用智能，该研究团队设计了基于集成衍射干涉混合设计和通用分布式计算架构的太极级大规模光子芯片，该架构具有数百万个神经元的能力，每秒每瓦(TOPS/W)的能量效率为160万亿次。太极实验上成功实现了片上1000个类别级别的分类任务，在包含1623个类别的Omniglot数据集中，取得了高达91.89%的准确率。同时，太极还展示了高保真的人工智能内容生成能力，将效率提升了两个数量级。
这一突破性的成果为大规模光子计算和高级任务的发展铺平了道路，进一步挖掘了现代AGI光子学的灵活性和巨大潜力。
据悉，对人工通用智能(AGI)的追求不断要求更高的计算性能。尽管集成光子电路具有优越的处理速度和效率，但其容量和可扩展性受到不可避免的误差的限制，因此只能实现简单的任务和浅层模型。
附：英文原文
Title: Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence
Author: Zhihao Xu, Tiankuang Zhou, Muzhou Ma, ChenChen Deng, Qionghai Dai, Lu Fang
Issue&Volume: 2024-04-12
Abstract: The pursuit of artificial general intelligence (AGI) continuously demands higher computing performance. Despite the superior processing speed and efficiency of integrated photonic circuits, their capacity and scalability are restricted by unavoidable errors, such that only simple tasks and shallow models are realized. To support modern AGIs, we designed Taichi—large-scale photonic chiplets based on an integrated diffractive-interference hybrid design and a general distributed computing architecture that has millions-of-neurons capability with 160–tera-operations per second per watt (TOPS/W) energy efficiency. Taichi experimentally achieved on-chip 1000-category–level classification (testing at 91.89% accuracy in the 1623-category Omniglot dataset) and high-fidelity artificial intelligence–generated content with up to two orders of magnitude of improvement in efficiency. Taichi paves the way for large-scale photonic computing and advanced tasks, further exploiting the flexibility and potential of photonics for modern AGI.
Source: https://www.science.org/doi/10.1126/science.adl1203