SUANPAN: Scalable Photonic Linear Vector Machine

Photonic linear operation is a promising approach to handle the extensive vector multiplications in artificial intelligence (AI) techniques due to the natural bosonic parallelism and high-speed information transmission of photonics. However, there is still no universal scalable photonic computing architecture that can be readily merged with existing electronic digital computing system. Even though it is believed that maximizing the interaction of the light beams is necessary to fully utilize the parallelism and tremendous efforts have been made in past decades, the achieved dimensionality of vector-matrix multiplication is very limited due to the difficulty of scaling up a tightly interconnected or highly coupled optical system. Here, we propose a programmable and reconfigurable photonic linear vector machine to perform only the inner product of two vectors, formed by a series of independent basic computing units, while each unit contains only one emitter-detector pair. The elemental values of the processed vectors are prepared by the time-space domain encoding. Specifically, one vector is encoded by the output duration of continuous light-emitter while the other is encoded as the position of the emitter-detector pair. The result of the inner product is obtained by the sum of photocurrents of all photodetectors. Since there is no interaction among light beams inside, extreme scalability could be achieved by simply multiplicating the independent basic computing unit without requiring large-scale analog-to-digital converter or digital-to-analog converter arrays. Our time-space domain encoding architecture is inspired by the traditional Chinese Suanpan or abacus, and thus is denoted as photonic SUANPAN. As a proof of principle, SUANPAN architecture is implemented with an 8×8 vertical cavity surface emission laser (VCSEL) array and an 8×8 MoTe ₂ two-dimensional material photodetector array. The experimental computing fidelities for randomly generated vector inner products are all over 98% for 1-bit, 2-bit, 4-bit and 8-bit quantization and over 95% for 8~80 vector dimensionalities with 4-bit quantization. Two typical AI tasks of the Ising machine for non-deterministic polynomial-time (NP)-hard optimization problem and artificial neural network for visual perception are performed to demonstrate the ability of SUANPAN architecture. For the Ising problem, 1024-dimensional problems are successfully solved, which is the highest dimensional optical Ising machine with heuristic algorithm. For artificial neural network, a competitive classification accuracy of 84~88% is achieved for MNIST (Modified National Institute of Standards and Technology) handwritten digit dataset. We believe that our proposed photonic SUANPAN is capable of serving as a fundamental linear vector machine that can be readily merged with existing electronic digital computing system and is potential to enhance the computing power for future various AI applications.