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.

中文翻译：

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大规模光子chiplet Taichi赋能160TOPS/W通用人工智能

对通用人工智能（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卓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错误的限制，因此只能实现简单的任务和浅层模型。为了支持现代 AGI，我们设计了 Taichi——基于集成衍射干涉混合设计和通用分布式计算架构的大规模光子芯片，该架构具有数百万个神经元的能力，每瓦每秒可进行 160 兆兆次运算（TOPS/ W) 能源效率。 Taichi 实验性地实现了片上 1000 个类别级别的分类（在 1623 个类别的 Omniglot 数据集中测试准确率为 91.89%）和高保真人工智能生成的内容，效率提高了两个数量级。 Taichi 为大规模光子计算和高级任务铺平了道路，进一步开发了现代 AGI 光子学的灵活性和潜力。