Introduction to Tensor Processing Units
Hardware acceleration is the use of specialized hardware to perform computations more efficiently than general-purpose processors. In machine learning, hardware acceleration has become increasingly important as models have grown in size and complexity. Traditional CPUs are not optimized for the type of matrix multiplication that is at the core of many machine learning algorithms. As a result, training and inference can be slow and require a lot of energy.
One approach to hardware acceleration is the use of graphics processing units (GPUs). GPUs were originally designed for rendering graphics in video games, but they have become popular for machine learning due to their high parallelism and ability to handle large matrices. However, GPUs were not specifically designed for machine learning, and there are limitations to their performance for certain types of computations.
Tensor Processing Units (TPUs) are a specialized type of hardware accelerator designed specifically for machine learning. They were developed by Google to support their own machine learning workloads, and are now available to the public through the Google Cloud Platform. TPUs are designed to perform matrix multiplication and other machine learning operations more efficiently than CPUs or GPUs. They achieve this through a combination of custom-designed hardware and software optimizations.
TPUs are particularly well-suited to large-scale machine learning workloads. They can be used for both training and inference, and can scale up to thousands of processors for very large models. However, TPUs are not a silver bullet - they are most effective when used with models that are specifically designed to take advantage of their architecture. In addition, TPUs require a different programming model than CPUs or GPUs, which can take some time to learn.
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