What specialized hardware is widely used to accelerate machine learning model training and inference?

Graphical processing units (GPUs) are widely recognized for their ability to accelerate machine learning model training and inference due to their architecture, which is designed to handle parallel processing tasks efficiently. Unlike central processing units (CPUs), which are optimized for sequential processing and can execute a limited number of parallel tasks, GPUs consist of thousands of smaller cores that can process many operations simultaneously. This parallelism significantly speeds up the computation required for training complex machine learning models that involve large datasets and intricate calculations.

In the context of machine learning, tasks like matrix multiplications, which are common in deep learning, benefit immensely from the capabilities of GPUs. These devices are specifically engineered to handle the high-throughput demands of training neural networks, making them a preferred choice in both research and production environments.

While digital signal processors (DSPs) and field-programmable gate arrays (FPGAs) also offer specialized capabilities for specific applications, they are typically not as universally applied for general machine learning acceleration as GPUs. DSPs are mainly used in signal processing tasks, and FPGAs require more manual configuration and are used in niche applications, often for hardware acceleration in specific algorithms. CPUs, while versatile and crucial for a range of computing tasks, do not provide the same level