A Beginner’s guide to AI Processors: CPUs, GPUs, TPUs and LPUs - Deepstash
A Beginner’s guide to AI Processors: CPUs, GPUs, TPUs and LPUs

A Beginner’s guide to AI Processors: CPUs, GPUs, TPUs and LPUs

Curated from: pytechacademy.medium.com

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AI Hardware

AI Hardware

As Artificial Intelligence (AI) technology continues to evolve, need for understanding the role of specialised hardware that power AI applications is important. From training machine learning models to processing big data, the choice of processor impacts the efficiency and performance of AI tasks. Whether you are a developer, data scientist, or tech enthusiast, this guide will help you to understand the most common types of AI hardware: CPUs, GPUs, TPUs, and LPUs.

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ALL-ROUNDER

Central Processing Unit (CPU) is the traditional heart of a computer, capable of handling a variety of tasks from basic calculations to complex software operations

ALL-ROUNDER

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More About CPU

Examples : Intel Core processors and Apple M1/M2 chips

Best for: General-purpose computing, smaller datasets, or tasks requiring complex logic where data processing needs are sequential.

Application: Running data preprocessing or light machine learning models like linear/logistic regression on a dataset with less number of data poin

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SPEEDSTER

Graphics Processing Units (GPUs) are known for their ability to handle multiple operations simultaneously in parallel, making them ideal for graphics rendering in Games and AI computations

SPEEDSTER

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More About GPU

Examples: Nvidia Tesla and Nvidia Drive PX

Best for: Large-scale machine learning tasks that requires handling of massive datasets with millions of data points or running complex deep learning models.

Application: Training a convolutional neural network on a dataset with millions of images. A task that could take days on a CPU might only take hours on a GPU due to its parallel processing capabilities.

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ACCELERATOR

Tensor Processing Units (TPUs) are Application Specific Integrated Circuits (ASIC), developed by Google, designed to accelerate TensorFlow, a popular machine learning framework.

ACCELERATOR

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More About TPU

Examples: Apple neural engine and Intel’s Gaudi AI processor

Best for: TPUs are built to enhance the speed and efficiency of TensorFlow applications, making them particularly suitable for tasks that can leverage this specific framework.

Application: TPUs are commonly used in advanced Google services like Google Search and Google Photos, where rapid data processing and model improvements are continously required.

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LANGUAGE EXPERT

Language Processing Units (LPUs) are specialised processors designed to excel in natural language tasks such as translation and sentiment analysis.

LANGUAGE EXPERT

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More About LPU

Groq, a new player, in the AI hardware industry, is pioneering the development of LPUs

Best for: LPUs are suitable for applications that require processing complex language data efficiently.

Example: LPUs power advanced chatbots, enabling them to understand and respond to human queries quickly and accurately, improving user interactions in customer service settings.

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Selecting the right AI hardware

Selecting the right AI hardware

CPUs are suitable for general-purpose computing and less complex AI tasks.

GPUs are the go-to for large-scale data processing and deep learning

TPUs offer the best performance for TensorFlow-based applications.

LPUs excel in handling optical data for imaging and real-time analysis.

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IDEAS CURATED BY

rajaathota72

Serial Entrepreneur | AI ML and Blockchain

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