What are TPU's

A Tensor Processing Unit (TPU) is a specialized hardware accelerator developed by Google for accelerating machine learning workloads, particularly those involving neural networks. TPUs are custom-designed application-specific integrated circuits (ASICs) optimized for performing large-scale matrix operations efficiently.

Here's an overview of what a TPU is and how it works:

1. Purpose-built for Machine Learning: Unlike general-purpose processors such as CPUs or GPUs, TPUs are specifically designed to excel in machine learning tasks. They are tailored to handle the computational demands of neural networks, which often involve large-scale matrix multiplications and other tensor operations.

2. Matrix Processing Architecture: The primary function of TPUs is matrix processing, which is a key component of many machine learning algorithms. TPUs contain thousands of multiply-accumulate units (MACs) arranged in a systolic array architecture. This architecture allows for highly parallelized computation of matrix operations, enabling TPUs to achieve high throughput on neural network calculations.

3. On-chip High-Bandwidth Memory (HBM): TPUs feature on-chip high-bandwidth memory (HBM), which allows for faster access to data and parameters compared to off-chip memory. This enables TPUs to handle larger models and batch sizes, leading to improved training efficiency and performance.

4. Streaming Data Processing: TPUs operate by streaming data into an infeed queue, loading data from the queue into HBM memory, performing matrix operations directly from HBM memory, and then storing the results in an outfeed queue. This streaming data processing approach minimizes the need for memory access during matrix operations, further enhancing computational throughput.

5. Compiler Optimization: Code that runs on TPUs must be compiled by the Accelerator Linear Algebra (XLA) compiler, which optimizes the computation graph emitted by machine learning frameworks like TensorFlow. The XLA compiler translates the computation graph into TPU machine code, optimizing the execution of linear algebra, loss, and gradient components of the graph for efficient processing on TPUs.

Overall, TPUs provide a highly efficient and scalable platform for accelerating machine learning workloads in the cloud. They are particularly well-suited for tasks dominated by matrix computations, such as training deep neural networks, and offer significant performance improvements over traditional processors like CPUs and GPUs for these types of workloads.