GPUs (Graphics Processing Units) are versatile, general-purpose parallel processors, originally designed for rendering graphics. TPUs (Tensor Processing Units) are custom-built ASICs (Application-Specific Integrated Circuits) developed by Google specifically for accelerating neural network computations. While GPUs excel at a wide range of parallel tasks, TPUs are highly specialized for the matrix and tensor operations that are fundamental to deep learning, often providing superior performance-per-watt for those specific workloads.
GPUs became popular for scientific computing and machine learning in the 2000s due to their parallel processing capabilities. Google introduced the first TPU in 2016, designed to accelerate their internal AI workloads, particularly those built with TensorFlow. This marked a shift towards creating hardware specifically tailored for the demands of deep learning.
GPUs, particularly from NVIDIA, are the dominant hardware for machine learning research and development, supported by a mature ecosystem of software and tools like CUDA. TPUs are primarily accessible through Google Cloud Platform and are widely used for large-scale training and inference of Google's own models. The choice between them often depends on the specific workload, the desired level of performance, and the software ecosystem.