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