# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 1 Introduction  
The capacity of computing systems is in an arms race with the massively growing amount of data. The AIC is considered to be an effective way to embrace the data explosion era and provides excellent ability in data computing. However, the traditional EAIC is based on electronic computing, which is gradually entering the bottleneck period with the upcoming limit of Moore's law [1, 2]. Integration size and large power consumption bring big challenges in calculation ability and data processing capacity for EAIC. Unlike electrical interconnects in EAICs, the PAIC is constructed by optical waveguide interconnects, forming optical neural networks (ONN) or optical elemental devices to realize optical linear or nonlinear computing. It offers great potential for orders of magnitude improvement in energy conservation and computing capacity due to its natural parallel processing, being less susceptible to interference, free superposition, etc. [3]. In addition, with the growth of nanophotonics technology, the PAIC has been applied in various practical scenarios, assisted by the prospect of an algorithm and material [4, 5, 6].  
PAICs include the hardware's structural design and the matched optimization algorithm. The hardware's structure refers to accomplishing the computing by changing the phase and intensity of the light signal through optical devices. The matching algorithm refers to data training and module control. In PAIC design, the compatibility of the structure and algorithm is also significant. Before fabrication, in order to obtain a workable PAIC, a system-level simulation platform is necessary to simulate the whole performance.  
The hardware structure of PAIC is also called ONN. The ONN in PAIC is constructed with optical waveguides or devices, such as Mach-Zehnder interferometers (MZI), semiconductor optical amplifiers (SOAs), micro-ring resonators (MRRs) or some 3D waveguides. Based on the hardware ONN structure or specialized devices, PAIC can easily implement linear or nonlinear operation [7]. Assisted by the weight training algorithm, PAIC highly benefits big data services and high-capacity communication networking.  
Specifically, the main advantages of PAICs are as follows: (1) PAICs utilizing optical signal propagation show a high clock frequency and, according to the waveguide interconnects, the data can be labeled from one dimension to multi-dimension [3]. These contribute to fast data processing and low latency [8]. (2) The data propagation and calculation take place simultaneously. Thus, it reduces the data's repeated handling and eliminates the \"memory wall\" issue. (3) The flexible multiple waveguide channel design represents a truly parallel implementation of PAICs, expanding the data capacity and also accelerating the processing speed. (4) The calculation in PAICs is achieved by a passive optical waveguide. There is no need for an extra energy supply and PAICs are cost-effective in energy consumption [6].  
In this invited paper, firstly, three linear ONNs are listed and we made a comparison concerning their advantages and disadvantages. Next, two nonlinear operation hardware structures are presented. Then, the current widely used training algorithm (gradient descent algorithm) is introduced. After that, the simulation platform of the PAIC system is described. Finally, the research work of our group in the related field is presented. An ONN structure is designed and an algorithm is optimized to form PAICs. It can complete artificial intelligence computing, such as image recognition and classification. The test results show that it runs well.