As
multinational and domestic enterprises increasingly transition to cloud
services providers (CSPs) and colocation solutions, the data center market in
China continues to evolve. The demand for data centers in the country has now
exceeded the available supply as organizations seek enhanced connectivity and
scalable solutions for their growing businesses. Investments by the Chinese
government for stimulating technological developments have led to an increase
in the adoption of cloud-based services, such as Big Data Analytics and
Internet of Things (IoT). Various government reforms, such as the establishment
of free trade in Shanghai, are attracting international investors. The growing
demand for high-density, redundant facilities is triggering a shift in the
design and development of the country’s data centers.
The global data center
accelerator market size is projected to grow from USD 13.7 billion in 2021 to USD 65.3
billion by 2026; it is expected to grow at a CAGR of 36.7% from 2021 to 2026.
Factors such as growing demand for deep learning and surge in demand for
cloud-based services are driving the growth of the market during the forecast
period.
Driver: Growth of cloud-based services
Deep
learning services being made available over the cloud are reducing the initial
costs associated with executing business operations and curtailing server
maintenance tasks. A growing number of tech giants and startups have begun
offering machine learning as a cloud service due to the burgeoning demand for
AI-based computation. Most companies and startups do not develop their own
specialized hardware or software to apply deep learning to their specific
business needs. Cloud-based solutions are ideal for small and midsized
businesses that find on-premises solutions costlier. Thus, the increasing
adoption of cloud-based technology is necessitating the need for deep learning.
Big data
analytics has also played a pivotal role in the growth of cloud services. Big
data analytics is the process of scrutinizing large datasets to uncover hidden
patterns, unknown correlations, market trends, customer preferences, and other
actionable insights. Big data has become important to many public and private
organizations wherein massive amounts of domain-specific information is
generated, which can contain useful information on national intelligence,
cybersecurity, fraud detection, marketing, and medical informatics. The deep
learning technique is used to extract high-level, complex abstractions from
data through a hierarchical learning process. It is an important technique used
for analyzing massive amounts of unsupervised data, making it a valuable tool
for big data analytics wherein the raw data is largely unstructured. Deep
learning is also used for extracting complex patterns from massive volumes of
data, semantic indexing, data tagging, fast information retrieval, and
simplifying discriminative tasks.
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The
evolution of technologies, namely machine learning and artificial intelligence
(AI), has generated the demand for cognitive computing technology across
various verticals such as automotive, industrial, and consumer. Rapid
developments in the video analytics domain and increasing adoption of advanced
technologies in the security and surveillance industry have resulted in the
development of high-performance AI-capable processors such as GPU and TPU,
which have higher memory bandwidth and computational capability as compared to
traditional processors, i.e., central processing units (CPUs). Creative
professionals, gamers, designers, and video enthusiasts require deep learning
accelerators with parallel processing capabilities that can facilitate the
provisioning of on-demand machine learning for augmented reality, virtual
reality, and several other application areas.
Restraint: Limited AI hardware expertss
AI is a
complex system, and for developing, managing, and implementing AI systems,
companies require personnel with certain skill sets. For instance, people
dealing with AI systems should be aware of technologies such as cognitive
computing, ML and machine intelligence, deep learning, and image recognition.
In addition, integrating AI solutions with existing systems is a difficult task
that requires well-funded in-house R&D and patent filling. Even minor
errors can translate into system failure or malfunctioning of a solution, which
can drastically affect the outcome and desired result.
Professional
services of data scientists and developers are needed to customize existing
ML-enabled AI processors. AI is a technology that is still growing and
emerging, and hence workforce possessing in-depth knowledge of this technology
is limited. The impact of this restraining factor will likely remain high during
the initial years of the forecast period.
Opportunity: Demand in the market for
FPGA-based accelerators
An FPGA is
an integrated circuit that can be configured by a customer or designer after it
is manufactured (field programmable). FPGAs are programmed using hardware
description languages such as VHSIC hardware description language (VHDL) or
Verilog. FPGAs offer advantages such as rapid prototyping, short time to
market, ability to be reprogramed in the field for debugging, and long product
life cycle. They contain individual programmable logic blocks known as
configurable logic blocks (CLBs). These logic blocks are interconnected in such
a manner that a user can configure the computing system multiple times. FPGAs
contain large resources of logic gates and RAM for complex digital computation.
In 2017,
Intel (US) acquired field-programmable gate array (FPGA) chip designer Altera
(US). With this, Intel is expected to further leverage FPGA accelerators into
its primary data center server business. In May 2020, Aldec, Inc., a pioneer in
mixed HDL language simulation and hardware-assisted verification for FPGA and
ASIC designs, has launched a new FPGA accelerator board for high-performance
computing (HPC), high-frequency trading (HFT) applications, and high-speed FPGA
prototyping. The HES-XCKU11P-DDR4 is a 1U form factor board featuring a Xilinx
Kintex® UltraScale+™ FPGA, a PCIe inference, and two QSFP-DD connectors
(providing a total of up to 400 Gbit/s bandwidth), and which hits the ideal
sweet spot between speed, logic cells, low power draw, and price.
Challenge: Unreliability of AI algorithms
AI is
implemented through machine learning using a computer to run specific software
that can be trained. Machine learning can help systems process data with the
help of algorithms and identify certain features from that dataset. However, a
concern associated with such systems is that it is unclear as to what is going
on inside algorithms; the internal workings remain inaccessible, and unlike
humans, the answers provided by these systems are uncontextualized. Researchers
at the Facebook AI Research (FAIR) lab found that the chat bots they created
had deviated from their predefined script and were communicating in a language
created by themselves, which humans could not understand. While one of the
important goals of current research is to improve AI-to-human communication,
the possibility that an AI system can create its own unique language that
humans cannot understand could be a setback. Moreover, several scientists and
tech influencers, such as Stephen Hawking, Elon Musk, Bill Gates, and Steve
Wozniak, have already warned that future AI technology could lead to unintended
consequences.
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