There are several types of AI chips available on the market. Each designed to accelerate different aspects of artificial intelligence computations. The landscape and advancement of technology is constantly evolving with new AI chip features and uses emerging. The choice of an AI chip depends on the specific use, performance requirements, power constraints, and budget considerations.
Here are some common AI chip types and a small explanation of their applications:
1
Graphics Processing Units (GPUs):
- How they work: Originally designed for rendering graphics in gaming and visualization. They excel in handling complex mathematical operations, making them suitable for AI workloads.
- Use cases: They can be used for various AI applications like image and speech recognition, natural language processing, and autonomous vehicles.
- How they work: Originally designed for rendering graphics in gaming and visualization. They excel in handling complex mathematical operations, making them suitable for AI workloads.
- Use cases: They can be used for various AI applications like image and speech recognition, natural language processing, and autonomous vehicles.
2
Central Processing Units (CPUs):
- How they work: CPUs are general-purpose processors that power most computers. While not specifically designed for AI, they can still perform AI computations using standard instructions.
- Use cases: CPUs are used for general AI tasks, suitable for lightweight items and running AI models on personal devices.
- How they work: CPUs are general-purpose processors that power most computers. While not specifically designed for AI, they can still perform AI computations using standard instructions.
- Use cases: CPUs are used for general AI tasks, suitable for lightweight items and running AI models on personal devices.
3
Tensor Processing Units (TPUs):
- How they work: TPUs are custom-designed AI accelerators (developed by Google). They are optimized for processing tensor operations, which are prevalent in deep learning.
- Use cases: TPUs are highly efficient for running inference on pre-trained machine learning models, and they are widely used in Google's cloud-based AI services.
- How they work: TPUs are custom-designed AI accelerators (developed by Google). They are optimized for processing tensor operations, which are prevalent in deep learning.
- Use cases: TPUs are highly efficient for running inference on pre-trained machine learning models, and they are widely used in Google's cloud-based AI services.
4
Field Programmable Gate Arrays (FPGAs):
- How they work: FPGAs are hardware devices that can be customized and reprogrammed to perform specific tasks. They offer parallel processing capabilities and can be tailored for various applications.
- Use cases: FPGAs are often used for accelerating specific AI workloads like video and image processing, due to their ability to be optimized for the task at hand.
- How they work: FPGAs are hardware devices that can be customized and reprogrammed to perform specific tasks. They offer parallel processing capabilities and can be tailored for various applications.
- Use cases: FPGAs are often used for accelerating specific AI workloads like video and image processing, due to their ability to be optimized for the task at hand.
5
Application-Specific Integrated Circuits (ASICs):
- How they work: ASICs are custom-designed chips built for a specific application or task, offering superior performance and energy efficiency compared to general-purpose processors.
- Use cases: AI-specific ASICs are developed to excel in deep learning tasks, used in data centers, and edge devices for various applications.
- How they work: ASICs are custom-designed chips built for a specific application or task, offering superior performance and energy efficiency compared to general-purpose processors.
- Use cases: AI-specific ASICs are developed to excel in deep learning tasks, used in data centers, and edge devices for various applications.
6
Neural Processing Units (NPUs):
- How they work: NPUs are specialized AI chips designed explicitly for processing neural networks efficiently.
- Use cases: NPUs are often used in smartphones to accelerate AI tasks like facial recognition, voice assistants, and other real-time apps.
- How they work: NPUs are specialized AI chips designed explicitly for processing neural networks efficiently.
- Use cases: NPUs are often used in smartphones to accelerate AI tasks like facial recognition, voice assistants, and other real-time apps.
- Microsoft
- Amazon
- Apple
- IBM
- Dell
- Nokia
- LG
- Intel
- Samsung
- Nvidia
- AMD
- Toshiba
- Broadcom
- OctoML
- Western Digital
- CEVA
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