Global Artificial Intelligence Chips Market 2017-2021: Top Drivers and Forecasts by Technavio
18 July 2017 - 1:23AM
Business Wire
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
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.”
This report is available at a USD 1,000 discount for a
limited time only: View market snapshot before
purchasing
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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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About Technavio
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Technavio ResearchJesse MaidaMedia & Marketing ExecutiveUS:
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