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Core Viewpoint - The rapid development of large language models (LLMs) is pushing the limits of contemporary computing hardware, necessitating exploration of alternative computing paradigms such as photonic hardware to meet the increasing computational demands of AI models [1][4]. Group 1: Photonic Hardware and Its Advantages - Photonic computing utilizes light for information processing, offering high bandwidth, strong parallelism, and low thermal dissipation, which are essential for next-generation AI applications [4][5]. - Recent advancements in photonic integrated circuits (PICs) enable the construction of fundamental neural network modules, such as coherent interferometer arrays and micro-ring resonator weight arrays, facilitating dense matrix multiplication and addition operations [4][5]. - The integration of two-dimensional materials like graphene and transition metal dichalcogenides (TMDCs) into silicon-based photonic platforms enhances the functionality of modulators and on-chip synaptic elements [5][31]. Group 2: Challenges in Mapping LLMs to New Hardware - Mapping transformer-based LLM architectures to new photonic hardware presents challenges, particularly in designing reconfigurable circuits for dynamic weight matrices that depend on input data [5][6]. - Achieving nonlinear functions and normalization in photonic or spintronic media remains a significant technical hurdle [5][6]. Group 3: Key Components and Technologies - Photonic neural networks (PNNs) leverage various optical devices, such as micro-ring resonators and Mach-Zehnder interferometer arrays, to perform efficient computations [9][13]. - The use of metasurfaces allows for high-density parallel optical computations by modulating light properties through sub-wavelength structured materials [14][16]. - The 4f optical systems enable linear filtering functions through Fourier transformation, integrating deep diffraction neural networks into optical architectures [20][21]. Group 4: Integration of Two-Dimensional Materials - The integration of graphene and TMDCs into photonic chips is crucial for developing high-speed and energy-efficient AI hardware, with applications in optical modulators, photodetectors, and waveguides [31][35][36]. - Graphene's exceptional optical and electronic properties, combined with TMDCs' tunable bandgap, enhance the performance of photonic devices, making them suitable for AI workloads [31][32]. Group 5: Future Directions and Challenges - The scalability of integrating two-dimensional materials poses challenges due to their fragility, necessitating advancements in transfer techniques and wafer-scale synthesis [45]. - Material stability and the complexity of integration with existing CMOS processes are critical factors that need to be addressed for widespread adoption of these technologies [45][46].