Systematic Analysis on TPU’s History and Key Technologies Behind It

With the rapid growth of artificial intelligence and deep learning, traditional computing architectures face limitations in efficiency and scalability.This paper explores Google's Tensor Processing Unit (TPU) to understand how specialized hardware accelerates AI workloads and drives next-generation computational innovations.This article analysis the development and key technologies behind Google's TPU.It begins by explaining how Google created the TPU to meet rising computational demands for deep learning, which CPUs and GPUs could not efficiently handle.The paper outlines the main components of a TPU, such as the systolic array, high-bandwidth memory, and matrix multiply unit.It then traces the evolution of TPU versions from v1 to v4, highlighting improvements in speed, cooling, and scalability.Key technologies like matrix multiplication optimization, low-precision computing, and the use of TensorFlow are also discussed.Finally, the impact of TPUs on both Google's services and broader AI research is summarized, showing how they make highperformance computing more accessible and efficient.