At a high level, a CPU (Central Processing Unit) is a general-purpose processor that can handle a wide variety of tasks, while a GPU (Graphics Processing Unit) is specialized for performing the complex mathematical calculations required for graphics rendering. A TPU (Tensor Processing Unit) is similar to a GPU but is specifically designed for accelerating machine learning workloads, particularly those used in neural networks.
From an AI perspective, the main difference between these types of processors is their ability to perform parallel computations. CPUs have a small number of cores (typically 4-8) that can handle a few instructions at a time, making them well-suited for tasks that involve a single thread of execution. GPUs, on the other hand, have a large number of cores (hundreds or thousands) that are optimized for performing the same operation on multiple pieces of data simultaneously, making them well-suited for tasks that can be parallelized such as image processing, deep learning, etc. TPUs are similar to GPU but are much more powerful and efficient, they are designed to handle the specific requirements of deep learning workloads which makes them a perfect fit for AI applications.
In summary, while a CPU is good at handling a wide variety of tasks, a GPU is better suited for performing complex mathematical calculations, and a TPU is specifically designed for accelerating machine learning workloads
