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Global Artificial Intelligence Chips Market 2017-2021: Top Drivers and Forecasts by Technavio

Technavio market research analysts forecast the global artificial intelligence (AI) chips market to grow at a CAGR of more than 54% during the forecast period, according to their latest report.

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Technavio has published a new report on the global artificial intelligence (AI) chips market from 20 ...

Technavio has published a new report on the global artificial intelligence (AI) chips market from 2017-2021. (Graphic: Business Wire)

The market study covers the present scenario and growth prospects of the global artificial intelligence (AI) chips market for 2017-2021. The report also lists GPUs, ASIC, FPGAs, and CPUs as the four major product segments.

According to Raghu Raj Singh, a lead analyst at Technavio for embedded systems research, “The high growth rate of hardware is due to the increasing need for hardware platforms with high computing power, which helps run algorithms for deep learning. The growing competition between startups and established players is leading the development of new AI products, both for hardware and software platforms that run deep learning programs and algorithms.”

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Technavio analysts highlight the following three market drivers that are contributing to the growth of the global AI chips market:

  • Heavy investment by companies in designing their own chips
  • Increasing implementation of AI in robotics
  • Use of AI in cyber security

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Heavy investment by companies in designing their own chips

AI is not only about software, but it also requires hardware to support different applications. Many companies are investing heavily in developing their chips that are designed for AI development. For instance, Google designed TPU, an ASIC that is specific to neural networks. It is a network of software and hardware that can learn individual tasks by analyzing large amounts of data. A challenge for ASIC is that it can perform only one function well. If another function is required, then it needs redesigning of the chip. The TPU contains a set of instructions that can help developers make changes to the existing codes and also develop new algorithms.

Another vendor that has already invested USD 2 billion during 2010-2016 in the R&D of AI chip is NVIDIA. In April 2017, NVIDIA developed a chip called Tesla P100, which is designed to provide more power in case of deep learning. These chips have more than 150 billion transistors, making it the world's largest chip. Tesla P100 has a neural network that can learn the data 12 times faster than the other chips of NVIDIA.

Increasing implementation of AI in robotics

Robotics is all about creating efficient and intelligent robots. Robotics involves the use of computer-controlled mechanical devices to perform specific tasks that are hazardous or tedious for humans.

“The contributions of AI in robotics include decision making, human-robot interaction, learning, perception, and reasoning. AI uses qualitative data to recognize the shapes of the objects, their ontologies, and their relationships for connecting the shapes with object names. The use of qualitative data helps in faster processing and automatic tagging,” says Raghu.

AI eliminates or reduces the risk to human life in many applications. Powerful AI software is used to develop high-precision capabilities for robots, which makes them free from human control and results in increased productivity. Such AI software is incorporated on chips that use neural networks. When a robot interacts with the real world, it gathers data through its sensors, and the neural networks compare these inputs with desired outputs. Therefore, the effectiveness of robots lies in the accuracy of the coding about the real world.

Use of AI in cyber security

Cyber threats are increasing in frequency and complexity. Cyber attackers are using automation technologies to carry out these attacks. Many organizations use manual efforts to prevent threats by analyzing internal security findings and then combining that with the external threat information. Such traditional methods take weeks or months to detect intrusions, and attackers can take advantage of this time lapse to extract data.

Organizations are now using AI for their day-to-day operations to counter cyber-attacks. Darktrace, a UK-based company, uses machine learning to track cyber-attacks. It has developed a system called Antigena, which uses the AI capabilities. Antigena has AI chips that are pre-programmed to automatically respond and takes actions to neutralize a threat as soon as a threat is identified. It acts as a digital antibody by stopping the devices or connections and reducing the speed within the network, thereby protecting the business operations.

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Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavio’s report library consists of more than 10,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio’s comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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