How does Google's TPU optimize machine learning workloads?

The answer focuses on the function of Google's Tensor Processing Units (TPUs) in machine learning workloads. TPUs are specialized hardware designed to significantly speed up the computation involved in training machine learning models. They are optimized for matrix operations, which are a core part of many machine learning algorithms, particularly deep learning models. By accelerating these computations, TPUs allow models to be trained more quickly and efficiently compared to traditional CPUs or GPUs.

In the context of machine learning, faster computation leads to shorter training times, enabling researchers and developers to iterate more rapidly, experiment with larger datasets, and deploy models more efficiently. This capability is particularly valuable for complex models that require substantial computational resources, as it directly addresses performance bottlenecks during the training phase.

The other options do not reflect the primary function of TPUs. Reducing data storage requirements, minimizing hardware costs, or increasing complexity of algorithms do not align with the primary design and objective of TPUs, which is to enhance computational performance.