image: Figure | Architecture of SUANPAN. a, The schematic diagram of SUANPAN architecture, consisting of a series of independent emitter-detector pairs. Left insets show the schematic and microscope photograph of a single VCSEL. Right insets show the schematic and microscope photograph of a single MoTe2 PD. b, The optical image of the VCSEL array. c, The optical image of the MoTe2 PD array.
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Credit: Xue Feng et al.
Artificial intelligence is currently an active topic in both scientific research and commercial application as well as daily life. The linear operations of high-dimensional vectors are fundamental and dominant. It is known that vector operations can be readily accelerated by photons due to the natural parallelism of bosons. In the past decades, various photonic computing architectures have been demonstrated to perform vector matrix multiplication in optical domain. All these architectures perform vector matrix multiplications based on the interaction between light beams, which refers to coherent or incoherent superposition between different light beams through beam splitting, beam combining, diffracting, scattering, etc. However, as the optical matrix transformation is adopted, the basic units in the computing architecture, i.e. liquid crystal cells, beam splitters, meta-atoms, etc., would be tightly interconnected or highly coupled with each other due to the interaction. Thus, high-dimensional optical vector-matrix operations cannot be achieved by simply multiplicating these basic units, which significantly limits the scalability of the architecture.
In a new paper published in Light: Science & Applications, a team of scientists, led by Professor Yidong Huang from the Department of Electronic Engineering in Tsinghua University and their collaborators from Peking University, Berxel Photonics Company Ltd. and Shenzhen Technology University have proposed the SUANPAN architecture for optical inner product instead of optical matrix operations. Just like the transistors in an integrated circuit, the independent basic computing unit in such scheme contains only one emitter-detector pair and could be scaled up to form a photonic computing chip. The elemental values of two vectors are encoded on the output intensity of the light-emitters and the photoresponsivity of the photodetectors (PDs) by a brand-new Bit Encoding and Analog Detecting method without requiring large-scale ADC or DAC arrays. The photocurrent of the PD would be proportional to the multiplication of the light intensity and photoresponsivity, and the final result of the inner product can be obtained by the summation of all the photocurrents. Since there is no interaction among the propagating light beams of all emitter-detector pairs and only the output currents of all PDs are connected, such scheme is scalable by increasing the number of emitter-detector pairs with no additional loss or error as well as flexibly reconfigurable and programmable for different computational tasks.
As a proof of principle, the SUANPAN architecture is implemented by utilizing an 8×8 vertical cavity surface emission laser (VCSEL) array and an 8×8 MoTe2 two-dimensional (2D) material PD array. In experiment, the calculation fidelity of random vector inner product can be as high as >98% for various bit precisions (2-bit, 4-bit and 8-bit), and >95% for various vector dimensionalities (@4-bit precision). Furthermore, such implementation has been successfully reconfigured to perform two typical AI tasks, Ising machine and artificial neural network (ANN). A randomly generated 1024-dimensional Ising problem is successfully solved, which is the highest dimensionality of optical Ising machine with heuristic algorithm. Meanwhile, a competitive classification accuracy of 88% is achieved for ANN on MNIST handwritten digit dataset. It is believed that photonic SUANPAN is capable to serve as a fundamental linear vector machine and is potential to enhance the computing power for future various AI applications.
These scientists summarize the operational principles and advantages of SUANPAN:
“It breaks through the traditional mindset of obtaining optical matrix transformations through interaction of light beams. Instead, there is no interaction among those propagating light beams of all emitter-detector pairs. Therefore, the SUANPAN can be decomposed into emitter-detector pairs as independent computing units. The scalability, reconfigurability and programmability of the SUANPAN architecture are only based on the multiplication, recombination and modulation of emitter-detector pairs without any additional cost. Compared with optical matrix transformations through interaction between light beams, the SUANPAN possesses following advantages: (1) With massive and industrial multiplication of emitter-detector pairs, the SUANPAN can theoretically be infinitely scalable. (2) The SUANPAN can be flexibly reconfigured and programmed to perform various specific computing tasks. (3) Only correcting the intensity of light beam is required, and there is no requirement to correct the phase term. (4) Even if one emitter-detector pair is broken during fabrication or operation, other emitter-detector pairs would not be affected, and only the operating dimensionality would be decreased.”
“SUANPAN provides a promising solution for optoelectronic analog-digital hybrid computing. Large-scale DAC and ADC arrays are usually required in optoelectronic computing. However, with Bit Encoding and Analog Detecting paradigm, M-bit digital electronic signal is converted to analog with in a set of M emitter-detector pairs, while each emitter-detector pair only represents 1-bit information. Thus, no DAC is required. At the same time, only one ADC is required to convert the total photocurrent into electronic digital signal. Therefore, the Bit Encoding and Analog Detecting computing paradigm greatly reduces the heavy burden introduced by ADC and DAC. Actually, it is also an important issue for the scalability of the SUANPAN architecture.”
Journal
Light Science & Applications
DOI
10.1038/s41377-025-02059-7
Article Title
SUANPAN: scalable photonic linear vector machine
Media Contact
WEI ZHAO
Light Publishing Center, Changchun Institute of Optics, Fine Mechanics And Physics, CAS
zhaowei@lightpublishing.cn
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