What is an Artificial Intelligence Chip ? Everything you need to know

What is Artificial Intelligence Chip ? 



Artificial intelligence (AI) chips are specialized silicon chips, which incorporate AI technology and are used for machine learning. AI helps to eliminate or minimize the risk to human life in  many industry verticals. The need for more efficient systems to solve mathematical and computational problems is becoming crucial owing to the increase in volume of the data

Many of the smart/IoT devices you’ll purchase are powered by some form of Artificial Intelligence (AI)—be it voice assistants, facial recognition cameras, or even your PC. These don’t work via magic, however, and need something to power all of the data-processing they do. For some devices that could be done in the cloud, by vast data centers. Other devices will do all their processing on the devices themselves, through an AI chip.


Top Company Profiles : Advanced Micro Devices, Inc.,Alphabet Inc. (Google),Huawei Technologies Co., Ltd.,IBM Corporation,Intel Corporation,Micron technology, Inc.,NVIDIA Corporation,Qualcomm Incorporated,Samsung electronics Co., Ltd.,Xilinx, Inc

But what is an AI chip? And how does it differ from the various other chips you may find in a device? This article will highlight the importance of AI chips, the different kinds of AI chips that are used for different applications, and the benefits of using AI chips in devices.

The AI processing unit

While typically GPUs are better than CPUs when it comes to AI processing, they’re not perfect. The industry needs specialized processors to enable efficient processing of AI applications, modelling and inference. As a result, chip designers are now working to create processing units optimized for executing these algorithms. These come under many names, such as NPU, TPU, DPU, SPU etc., but a catchall term can be the AI processing unit (AI PU).

The AI PU was created to execute machine learning algorithms, typically by operating on predictive models such as artificial neural networks. They are usually classified as either training or inference as these processes are generally performed independently.

Some applications we already see in the real world:

·      1. Monitoring a system or area from threats like a security system involving real time facial            recognition (IP cams, door cameras, etc.)


       2. Chatbots for retail or businesses that interact with customers


       3. Natural language processing for voice assistants

 

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