What is TPU

Machine learning is rapidly revolutionizing all industries as it allows computers to learn from data on their own, instead of being programmed. To improve accuracy and optimize performance, machine learning models require sophisticated neural networks that can achieve complex decision-making. But, building such models is not easy, as it requires operating through multiple layers of network architecture that keep on growing to capture diverse patterns within the data.

This is where TPU, or Tensor Processing Unit, comes into play. TPU is a specialized hardware device developed by Google to accelerate machine learning tasks. It is an Application-Specific Integrated Circuit (ASIC) designed to solve complex matrix operations required for training and inference tasks of machine learning models quickly and efficiently.

TPUs support Google’s custom TensorFlow framework, which enables them to perform multiple arithmetic operations, including multiply, add, and matrix multiplication, in parallel. Tensor processing units are customized to work with TensorFlow’s graph nodes, such as convolutional, batch normalization, and fusion operators, to provide high-performance computation of subgraphs.

In comparison to traditional CPUs and GPUs, TPUs are more efficient as they can perform matrix-wise computations in parallel. This helps to improve application performance speed by reducing latency in computation. They are also designed to work on 8-bit and 16-bit precision, which speeds up the training cycle for machine learning tasks.

Another advantage of TPUs is that they can handle large models and massive training sets, which cannot be efficiently processed by CPUs and GPUs. They support large models and training sets by using different types of virtual machines based on the number of TPU’s cores required, which enables developers to train larger scale models efficiently.

In conclusion, Tensor Processing Units (TPUs) are specialized hardware devices designed for machine learning workloads that are accelerated by performing tensor operations. It is a powerful tool used by data scientists and machine learning engineers to train complex neural network models in a faster and efficient way. TPUs are being increasingly adopted by many companies as it helps teams to save time and have more accurate predictions in less time, and with less cost than traditional methods.