# A Survey on Hardware Accelerators for Large Language Models
## 1 Introduction  
Large language models (LLMs), such as GPT-3, are a class of artificial intelligence systems designed to understand, generate, and process human language with a level of complexity and nuance previously unattainable. These models are trained on extensive datasets, encompassing a broad spectrum of human discourse, which enables them to perform a wide array of language-related tasks, from translation and summarization to conversation and content creation. Over the past few years, the field of LLMs has witnessed remarkable evolution, characterized by significant advancements in model architecture, training methodologies, and data processing capabilities. This evolution has been driven by breakthroughs in deep learning and the increasing availability of large-scale computational resources. The impact of LLMs on society and business is profound and multifaceted. They have revolutionized the way we interact with technology, introducing new efficiencies and capabilities in areas such as customer service, content generation, and decision support systems. Moreover, LLMs have become instrumental in driving innovation in various sectors, including healthcare, finance, and education, by providing enhanced capabilities for data analysis, pattern recognition, and predictive modeling. This transformative influence underscores the importance of exploring and understanding the underpinnings of these models, their operational mechanisms, and the implications of their widespread application in diverse domains. Hardware acceleration plays a pivotal role in advancing large language modeling (LLM) by optimizing computational efficiency and accelerating training and inference. The field is currently focused on developing specialized hardware, improving software-hardware integration, and exploring novel architectures to enhance performance. Severalarchitectures have been proposed that are focused on utilizing specialized hardware, memory optimizations, and increasing energy efficiency using the accelerators. Specialized hardware: Various hardware accelerators are employed to optimize the performance of large language models (LLMs), each with unique strengths and applications:  
* Graphics processing units (GPUs): GPUs, such as NVIDIA's A100 and H100, are the most widely used accelerators for training and inference in LLMs. Their high parallelism and specialized tensor cores make them ideal for matrix multiplication, which is fundamental to deep learning. GPUs are versatile and widely supported by software frameworks such as PyTorch and TensorFlow, excelling in both training and inference scenarios. * Field-programmable gate arrays (FPGAs): FPGAs are customizable chips that can be reprogrammed for specific tasks, offering flexibility and efficiency for AI workloads. They are particularly useful in scenarios requiring tailored optimization, such as low-latency inference or energy-efficient deployments. However, their adoption for LLMs is limited due to higher programming complexity compared to GPUs. * In-memory computing: In-memory computing is an emerging paradigm that integrates computation directly within memory cells, reducing the data movement bottleneck. This approach shows promise for tasks involving large-scale matrix operations and is being explored for energy-efficient LLM inference. While still in its early stages, IMC could overcome the limitations of traditional memory bandwidth. * Application-specific integrated circuits (ASICs): ASICs are custom-designed chips tailored for specific AI workloads, offering unmatched efficiency and performance. Examples include Google's tensor processing units (TPUs) and Cerebras' wafer-scale engines. These accelerators are optimized for large-scale training and inference, with specialized architectures that maximize throughput and energy efficiency. However, their lack of versatility and high development costs limit their widespread use.