Abstract
AI chips are specialized hardware components designed to accelerate AI operations, making them crucial for various industries. This study provides a comprehensive overview of artificial intelligence (AI) chips, their structure, principles, fabrication process, applications, limitations, and future outlooks. The article discusses the components commonly found in AI chip structures, including processing units, memory hierarchy, interconnects, and specialized hardware units. It delves into the principles guiding AI chip design, such as specialization, parallelism, low precision arithmetic, and hardware-software co-design. The fabrication process of AI chips involves intricate steps, including design, mask generation, deposition, lithography, and testing. Applications of AI chips span data centers, edge computing, autonomous vehicles, healthcare, NLP, and more. Despite their advancements, AI chips have limitations, such as energy efficiency and specificity, but the future holds promise with improvements in energy efficiency, specialized acceleration, hybrid architectures, and quantum AI chips. Overall, AI chips have transformed the AI landscape, setting the stage for continued innovation and impact in various sectors.
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