Tensor Processing Unit

Tensor Processing Units (TPUs) are specialized hardware accelerators designed to significantly speed up machine learning computations, primarily focusing on the matrix multiplications central to deep neural networks. Current research emphasizes optimizing TPU performance for various model architectures, including convolutional neural networks (CNNs), graph neural networks (GNNs), and generative adversarial networks (GANs), through techniques like precision reduction, reconfigurable dataflows, and algorithm-hardware co-design. This focus on efficiency and scalability makes TPUs crucial for accelerating diverse applications, from real-time object detection in robotics to large-scale graph embeddings and high-throughput AI training.