TPUs (Tensor Processing Units)
See GPUs (Graphics Processing Units)
TLDR: TPUs (Tensor Processing Units) are specialized hardware accelerators designed by Google in 2016 to optimize the performance of machine learning workloads, particularly for neural network training and inference. These processors are highly efficient at performing tensor operations—the mathematical computations fundamental to deep learning algorithms. TPUs are integral to powering large-scale AI systems and applications like natural language processing and image recognition.
https://en.wikipedia.org/wiki/Tensor_Processing_Unit
The architecture of TPUs is tailored for high-throughput matrix multiplications and reduced precision arithmetic, such as 8-bit integer operations, to deliver exceptional performance while minimizing energy consumption. Unlike traditional GPUs and CPUs, which are general-purpose, TPUs are designed explicitly for accelerating frameworks like TensorFlow. They feature custom dataflow architectures that handle complex tensor computations efficiently, enabling faster training times for large AI models like GPT and BERT.
TPUs are widely used in Google Cloud services, allowing researchers and enterprises to scale their AI workloads cost-effectively. Advanced iterations, such as the TPU v4 introduced in 2021, provide up to 10 petaflops of processing power, further enhancing their capability for cutting-edge machine learning applications. By delivering unmatched efficiency and scalability, TPUs have become a cornerstone of modern AI infrastructure, shaping the development of intelligent technologies.