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Студенческий форум -> Студенческие форумы НИЯУ МИФИ -> Магистры МИФИ и РУДН: программы, тематика курсов, советы -> Edge AI Technology+ Hardware+Chips

(2023) A Brief Introduction to Hardware Behind AI

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VAL

Дата 12.03.2024 18:45

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(2023) A Brief Introduction to the Hardware Behind AI
Источник: https://geekflare.com/hardware-behind-ai/

QUOTE

The heart of AI hardware lies in the processors such as Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and Neural Processing Units (NPUs).

GPUs: These were initially designed for rendering graphics. Since GPUs excel in parallel processing, these are perfect for training AI models.

TPUs: Created by Google specifically for accelerating AI computations, TPUs particularly excel in deep learning tasks.

NPUs: These can handle tasks involving neural networks and essentially mimic the neural connections found in the human brain.

All the above hardware components work together to process and analyze vast amounts of data, enabling AI systems to learn, adapt, and make predictions.

Contents:
- What Is AI Hardware?
- AI Hardware Technologies
- AI Hardware vs. Regular Hardware
- How Startups Are Adopting AI Hardware
- Best AI Hardware Providers
- Advancements and Innovations in AI Hardware
- Pros and Cons of Using AI Hardware

Это сообщение отредактировал VAL - 13.03.2024 04:26

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Дата 13.03.2024 04:33

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Cerebras has significantly contributed to AI hardware through its Wafer Scale Engine (WSE).

Developed by Google, Edge TPU is an ASIC that has been purpose-built for running AI at the edge.

QUOTE

Quantum Computing
The potential of quantum computers to tackle complex problems surpasses the capabilities of classical computers by leaps and bounds. While we are in the initial stage of witnessing the practical applications of quantum computing in AI, the impact it will have on AI hardware is profound.

Edge AI Acceleration
The rise of edge computing is being accelerated by AI hardware specifically designed for real-time, energy-efficient processing. This technological progress holds significant relevance, especially for devices such as IoT sensors and wearables.

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Memory Innovations
Are you familiar with how AI algorithms work? They can be quite memory-intensive, which means they require a lot of storage space.

Fortunately, there are innovative solutions available to address this issue. Two emerging memory technologies, called resistive RAM (ReRAM) and phase-change memory (PCM), are stepping in to bridge the gap.

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Pros
- Enhanced performance: AI hardware can handle complex AI tasks, offering faster and more efficient processing compared to traditional hardware.
- Efficiency: Some AI chips, such as TPUs and neuromorphic chips, are made energy efficient. By using these specialized chips, you’re saving money on operations and being kinder to the environment.
- Speed: AI hardware significantly speeds up data processing and model training, empowering you to gain faster insights and make real-time decisions in various scenarios.
- Complex problem solving: Quantum computing, a type of AI hardware, has the incredible ability to solve complex problems at an unprecedented speed.
- Scalability: AI hardware can adapt and expand to accommodate the increasing demands related to growing datasets and evolving AI applications.

Cons
- Cost: The initial investment in AI hardware, including development, deployment, and maintenance costs, can be high.
- Lacks versatility: Some AI hardware, like ASICs, is optimized for specific tasks, limiting versatility for broader applications.
- Complex implementation: Integrating AI hardware requires both expertise and resources, which may pose challenges for smaller businesses during implementation.

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VAL	Дата 18.03.2024 08:05
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VAL	Дата 18.04.2024 10:02
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