CURRENT AFFAIRS
CURRENT AFFAIRS
Description
To understand how a Tensor Processing Unit (TPU) stands apart, it's helpful to first explore the roles of CPUs and GPUs in computing.
About CPU (Central Processing Unit)
The CPU is a general-purpose processor found at the core of nearly every computer system. It acts as the command center, managing instructions and coordinating tasks across all parts of the computer.
Early CPUs had only a single core, but modern processors come equipped with multiple cores typically between 2 to 16 that enable better multitasking. Each core handles one task at a time, and even though operations occur sequentially, CPUs handle them so quickly that users rarely notice.
CPUs are ideal for everyday applications like browsing, word processing, and running operating systems because of their flexibility and ability to manage a wide range of instructions efficiently.
About GPU (Graphics Processing Unit)
GPUs are designed to perform many calculations simultaneously, using parallel processing. Originally created to handle complex graphics rendering in gaming and multimedia, GPUs have evolved into powerful tools for high-performance computing.
With thousands of smaller cores, GPUs can break down large tasks into smaller chunks and process them at the same time. This makes them perfect for intensive operations like scientific simulations, video rendering, and machine learning workloads.
Although GPUs are far more powerful than CPUs in parallel tasks, they still rely on CPUs to manage system-level operations and non-parallel computations.
About TPU (Tensor Processing Unit)
A TPU is a custom-built processor created by Google specifically for machine learning. Unlike CPUs and GPUs, which are versatile and capable of handling a variety of computing tasks, TPUs are designed with a narrow focus: accelerating AI workloads.
TPUs specialize in handling "tensors," which are multi-dimensional data structures used in deep learning algorithms. These processors are optimized to efficiently train and run neural networks, enabling rapid development and deployment of AI models.
Introduced in 2015, TPUs are capable of performing operations that would take significantly longer on traditional processors. They are a key component in many of Google’s AI-driven services, such as Google Search, Translate, YouTube recommendations, and DeepMind’s advanced AI systems.
Summary: Key Differences
| Feature | CPU | GPU | TPU |
| Purpose | General-purpose computing | Graphics and parallel processing | AI and machine learning workloads |
| Processing Style | Sequential | Parallel | Highly parallel (AI-focused) |
| Core Count | Few (2–16 cores) | Thousands of smaller cores | Fewer but highly specialized units |
| Ideal Use | Everyday tasks, OS, logic | Rendering, simulations, ML tasks | Neural networks, AI model training |
| Developed By | Various manufacturers (Intel, AMD) | NVIDIA, AMD, Intel |
In conclusion, while CPUs manage general tasks and GPUs offer speed through parallelism, TPUs take things further by focusing exclusively on AI, offering unmatched efficiency in training and deploying deep learning models.
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