Northrook, IL -- (SBWIRE) -- 02/28/2020 -- The data center accelerator market was valued at USD 1.60 billion in 2017 and is expected to reach USD 21.19 billion by 2023, at a CAGR of 49.47% during the forecast period. In this report, 2017 has been considered as the base year, and the forecast period is from 2018 to 2023.

By processor type, GPU to hold the largest size of the data center accelerator market during the forecast period

The data center accelerator market, by processors type was valued at the highest CAGR during the forecast period. The growth of the market for FPGA is attributed to the increasing adoption of FPGAs for acceleration of enterprise workloads. With the exponential data growth, data center operators have to establish a balance between the need for the performance at scale and the operational efficiencies. To boost performance and power efficiencies, data centers are most widely adopting Intel Xeon Scalable processors to support data-intensive performance requirements. For instance, in December 2015, Intel Corporation (US) acquired Altera Corporation (US), a provider of FPGA technology to offer new product categories in the high-growth data center and IoT market segments.

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By application, enterprise inference to grow at the highest CAGR during the forecast period

The data center accelerator market has been segmented on the basis of application into deep learning training, public cloud inference, and enterprise inference. The market for enterprise inference is likely to reach the highest CAGR during the forecast period. Data center accelerator market for enterprise inference is expected to gain traction in the coming years on the back of the hyper-scale cloud-based companies such as Google Inc. (US), Facebook, Inc. (US), Amazon.com (US), which are more focused on digital transformation to build similar types of cloud-native applications. The cloud-native application development requires a new set of tools, methodologies, and underlying IT infrastructure. With the availability of both open-source and commercial software, the cloud-native architecture has been widely adopted by enterprises.

APAC to account for the highest CAGR during the forecast period

The data center accelerator market in APAC is estimated to grow at the highest CAGR during the forecast period. The organizations in APAC have more preference for deploying a hybrid cloud. The organizations are adopting a mix of on-premise, third-party, co-location, private cloud, hosted cloud, and public cloud—depending on nature of workloads, legacy decisions made by the team, budgets, technology maturity within the organization, etc.

Market Dynamics
Driver: Growing demand for AI in HPC data centers

Apart from chipmakers making accelerators, the companies that make data centers, including Dell EMC (US) and HP (US), are also emphasizing on integrating deep learning-based accelerators into their HPC data centers.

The growth in consumer generated data and rising use of AI-based services have led to the increased demand for AI-centric data centers. AI provides personalized services by understanding customer behavior data generated from CRM systems, product reviews, and media comments. NVIDIA's (US) reported GPU sales to data centers in Q3 2016 was worth USD 75 million, a large portion of which was attributed to HPC data centers. Later at 2017 Investor day presentation, NVIDIA provided an estimate that the market for accelerators aimed at data centers could touch USD 30 billion by 2020. This staggering number is 19 times of NVIDIA's current revenue from data centers, and it also shadows Intel's USD 17 billion in data center revenue from 2016. Though the forecast from NVIDIA looks overestimated in terms of market value, the company's impressive growth in the data center business segment in recent years gives a clear indication that the market is expected to witness an exponential growth in the coming years.

Restraint: Limited AI hardware experts

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, and this can drastically affect the outcome and desired result.

Professional services of data scientists and developers are needed to customize existing ML-enabled AI processors. Due to AI being a technology that is still in its early stage of life cycle, a 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.

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Opportunity: Rising need of coprocessors due to slowdown of Moore's Law

The Moore's law states that the number of transistors per square inch on integrated circuits will double about every eighteen months until at least 2020. In April 2015, Intel stated that it can keep the Moore's law going for another few years by developing 7nm and 5nm fabrication technologies. However, moving forward it would be hard to further reduce the size of processors as doing so would also reduce the space between electrons and holes, culminating into problems such as current leakage and overheating in ICs These problems would lead to slower performance, high power consumption by ICs, and reduced durability. Thus, the need to find an alternate way to increase the computational power of chips has fueled the development of accelerators or coprocessor chips.

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 un-contextualized. In July 2017, 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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