This paper provides a comprehensive survey of hardware accelerators designed to enhance the performance and energy efficiency of Large Language Models (LLMs). LLMs, such as GPT-3, have revolutionized natural language processing with their advanced capabilities in understanding and generating human-like text. However, the computational demands of these models pose significant challenges, particularly in terms of training and inference. The paper explores various hardware accelerators, including GPUs, FPGAs, ASICs, and in-memory computing, and evaluates their effectiveness in addressing these challenges. Key findings include: - **Computational Complexity**: LLMs require extensive computational resources due to their large number of parameters and complex neural network architectures. Both training and inference phases are computationally intensive. - **Energy Consumption**: The high computational requirements of LLMs lead to significant energy consumption, which has environmental implications. Efforts to optimize energy efficiency are ongoing. - **Accelerator Types**: - **FPGAs**: Offer high flexibility and customization, with some schemes achieving up to 200x speedup and significant energy savings. - **ASICs**: Provide the highest speedup and energy efficiency but require significant investment in fabrication. - **In-memory Computing**: Utilizes NVM and memristors to process data in-situ, offering potential for high performance and energy efficiency. - **GPUs**: While widely used, they may not be as efficient as other accelerators in certain tasks. The paper concludes that hardware accelerators are crucial for reducing the computational and energy demands of LLMs, and their continued development will be essential for future data centers to handle increasingly complex models.This paper provides a comprehensive survey of hardware accelerators designed to enhance the performance and energy efficiency of Large Language Models (LLMs). LLMs, such as GPT-3, have revolutionized natural language processing with their advanced capabilities in understanding and generating human-like text. However, the computational demands of these models pose significant challenges, particularly in terms of training and inference. The paper explores various hardware accelerators, including GPUs, FPGAs, ASICs, and in-memory computing, and evaluates their effectiveness in addressing these challenges. Key findings include: - **Computational Complexity**: LLMs require extensive computational resources due to their large number of parameters and complex neural network architectures. Both training and inference phases are computationally intensive. - **Energy Consumption**: The high computational requirements of LLMs lead to significant energy consumption, which has environmental implications. Efforts to optimize energy efficiency are ongoing. - **Accelerator Types**: - **FPGAs**: Offer high flexibility and customization, with some schemes achieving up to 200x speedup and significant energy savings. - **ASICs**: Provide the highest speedup and energy efficiency but require significant investment in fabrication. - **In-memory Computing**: Utilizes NVM and memristors to process data in-situ, offering potential for high performance and energy efficiency. - **GPUs**: While widely used, they may not be as efficient as other accelerators in certain tasks. The paper concludes that hardware accelerators are crucial for reducing the computational and energy demands of LLMs, and their continued development will be essential for future data centers to handle increasingly complex models.