The emergence of parallel convolution-operation technology has substantially powered the complexity and functionality of optical neural networks (ONN) by harnessing the dimension of optical wavelength. However, this advanced architecture faces remarkable challenges in high-level integration and on-chip operation. In this work, convolution based on time-wavelength plane stretching approach is implemented on a microcomb-driven chip-based photonic processing unit (PPU). To support the operation of this processing unit, we develop a dedicated control and operation protocol, leading to a record high weight precision of 9 bits. Moreover, the compact architecture and high data loading speed enable a preeminent photonic-core compute density of over 1 trillion of operations per second per square millimeter (TOPS mm−2). Two proof-of-concept experiments are demonstrated, including image edge detection and handwritten digit recognition, showing comparable processing capability compared to that of a digital computer. Due to the advanced performance and the great scalability, this parallel photonic processing unit can potentially revolutionize sophisticated artificial intelligence tasks including autonomous driving, video action recognition and image reconstruction.
Recent progress on optical neural networks (ONNs) heralds a new future for efficient deep learning accelerators, and novel, to the best of our knowledge, architectures of optical convolutional neural networks (CNNs) provide potential solutions to the widely adopted convolutional models. So far in optical CNNs, the data patching (a necessary process in the convolutional layer) is mostly executed with electronics, resulting in a demand for large input modulator arrays. Here we experimentally demonstrate an optical patching scheme to release the burden of electronic data processing and to cut down the scale of the input modulator array for optical CNNs. Optical delay lines replace electronics to execute data processing, which can reduce the scale of the input modulator array. The adoption of wavelength-division multiplexing enables a single group of optical delay lines to simultaneously process multiple input data, reducing the system complexity. The optical patching scheme provides a new solution to the problem of data input, which is challenging and concerned with the field of ONNs.
Tensor analytics lays the mathematical basis for the prosperous promotion of multiway signal processing. To increase computing throughput, mainstream processors transform tensor convolutions into matrix multiplications to enhance the parallelism of computing. However, such order-reducing transformation produces data duplicates and consumes additional memory. Here, we propose an integrated photonic tensor flow processor (PTFP) without digitally duplicating the input data. It outputs the convolved tensor as the input tensor ‘flows’ through the processor. The hybrid manipulation of optical wavelengths, space dimensions, and time delay steps, enables the direct representation and processing of high-order tensors in the optical domain. In the proof-of-concept experiment, an integrated processor manipulating wavelengths and delay steps is implemented for demonstrating the key functionalities of PTFP. The multi-channel images and videos are processed at the modulation rate of 20 Gbaud. A convolutional neural network for video action recognition is demonstrated on the processor, which achieves an accuracy of 97.9%.
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