Data from: Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence

The everlasting pursuit for a more powerful artificial general
intelligence (AGI) continuously demands for higher computing performance.
Breaking the restrictions from the Moore’s Law, integrated photonic neural
networks have shown great potential in terms of superior processing speed
and high energy-efficiency. However, the capacity and scalability are
restricted by the unavoidable and time-varying on-chip errors that only
simple tasks and shallow models (e.g., 10-category digits classification
with thousand-parameter-level networks) can be on-chip realized optically.
To support modern AGIs with optical computing, we innovate Taichi,
large-scale photonic chiplets owning millions-of-neuron on-chip computing
capability with 160 TOPS/W energy efficiency. For the first time, Taichi
experimentally realized on-chip optical neural networks (ONNs) with over
13.96M neurons for thousand-category-level classification (tested 91.89%
of accuracy for 1623-category Omniglot dataset) and high-fidelity
artificial intelligence-generated content (AIGC) with state-of-the-art
performances, while achieving up to 2 orders of magnitude improvement in
computing efficiency than state-of-the-art AI chips. Based on an
integrated diffractive-interference-hybrid design and a general
distributed computing architecture, Taichi paves its own way for
large-scale on-chip ONN models and advanced tasks, further exploiting the
flexibility and potential of optical devices, which will lead to a giant
leap in scalability, accuracy and efficiency to support modern AGI.