The AI infrastructure market is expected to grow from USD 23.7 billion in 2021 to USD 79.3 billion by 2026, at a CAGR of 27.3%. Increased data traffic and need for high computing power, increasing adoption of cloud machine learning platform, increasingly large and complex dataset, rising focus on parallel computing in AI data centers, and growing number of cross-industry partnerships and collaborations - are the key factors driving the AI infrastructure market.

Market Dynamics of AI Infrastructure

Driver: Increased data traffic and need for high computing power

GPU/CPU manufacturers, such as NVIDIA, AMD, Intel, Qualcomm, Huawei, and Samsung, have significantly invested in the development of chips that are compatible with AI solutions. Apart from CPUs and GPUs, application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) are also being developed for AI applications. For example, Google built a new ASIC called tensor processing unit (TPU).

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A compute-intensive chip is one of the critical parameters for processing AI algorithms; the faster the chip, the quicker it can process data required to create an AI system. Currently, AI chips are mostly deployed in data centers/high-end servers as end computers are currently incapable of handling such huge workloads and do not have enough power and time frame. NVIDIA has a range of GPUs that offer GPU memory bandwidth according to the application. For example, GeForce GTX Titan X offers a memory bandwidth of 336.5 GB/s and is mostly deployed in desktops, while Tesla V100 16 GB offers a memory bandwidth of 900 GB/s and is used in AI applications.

Effective storage, managing, and interpreting of data have become essential. According to the latest records of Cisco Systems, Inc., the global IP traffic will reach 1.2 ZB per year or 96 EB per month in 2021; the global IP traffic will reach 3.3 ZB per year. This signifies that global IP traffic is flourishing and is likely to generate more data in the near term. Hence, it becomes essential to have higher computing power devices.

Restraint: Lack of AI hardware experts and skilled workforce

AI is a complex system, and companies require experts and a skilled workforce for developing, managing, and implementing AI systems. For example, people dealing with AI systems should be aware of technologies such as cognitive computing, machine learning (ML), machine intelligence, deep learning, and image recognition. In addition, integrating AI technology into existing systems is a challenging 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. A workforce possessing in-depth knowledge of this technology is limited as AI as a technology is still in its early stage of the life cycle. The impact of this restraining factor is likely to remain high during the initial years of the forecast period.

Companies across industries embrace emerging technologies to improve operational efficiency and performance, reduce waste, conserve natural resources, reach new markets and audiences with speed and convenience, and support product and process innovation. Advancements in technology will significantly impact job availability and quality in the global economy—eliminating jobs, creating new jobs, and demanding new skills from workforces worldwide. Factories embracing machine vision technology would require more complex skill sets, and it could be difficult for lower-skilled, less-educated workers to access opportunities.

Opportunity: Surging demand for FPGA-based accelerators

Field Programmable Gate Array (FPGA) is an integrated circuit that a customer or designer can configure after it is being 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, the ability to be reprogramed in the field for debugging, and a 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 to perform complex digital computation. FPGAs are used as co-processors to offload work done in microcontrollers, digital signal processors, or any other host processor. FPGAs provide flexible interfacing and are optimized to complement host processors.

Challenge: Concerns regarding data privacy in AI platforms

AI has several applications in the healthcare industry. However, the adoption of AI in the industry is restricted to an extent owing to data privacy concerns. Patients’ health data is protected under federal laws in many countries, and any breach or failure to maintain its integrity can result in legal and financial penalties. As AI used for patient care requires access to multiple health datasets, it is essential for AI-based tools to adhere to all data security protocols mandated by governments and regulatory authorities. This is a challenging task as most AI platforms are consolidated and require extensive computing power owing to which patient data, or parts of it, can be required to reside in a vendor’s data center. This is a major challenge in the market. The figure provided below shows the percentage of healthcare breaches reported by the US Department of Health and Human Services, which has affected more than 500 individuals.