The adoption of AI is rising in various industries such as retail, supply chain, healthcare, BFSI, and others. For AI applications to function smoothly, a lot of computational power is required. AI hardware varies from the traditional hardware in terms of its computational power. The AI hardware ecosystem includes products such as Graphics Processing Unit (GPU), application specific integrated circuits, and others. Hardware manufacturers are focusing on introducing innovative hardware for AI applications. Google has developed a customized Application Specific Integrated Circuit (ASIC), known as Tensor Processing Unit (TPU). These processors are designed to support machine learning workloads. Such developments will contribute to the growth of the overall AI hardware ecosystem. The AI hardware Ecosystem is expected to grow at a CAGR of 20% between 2018 and 2023.

Figure 1: Ecosystem Snapshot: Artificial Intelligence (AI) Hardware Ecosystem

Leading hardware manufacturers such as Intel Corporation and Nvidia Corporation are competing with each other in the market. Intel has introduced its Nervana product line NNP-T1000 and NNP-I1000 in the year 2019. This processor was designed to compete with Nvidia’s NVDLA and Google’s TPU. In April 2019, Intel has announced its investment on 14 AI startups. The company has invested around US$ 117 Mn in total in hardware companies such as Untether AI, Zhuhai Eeasy Technology Co. Ltd, and others.

Other product launches in the AI processor market include Huawei’s Ascend 910 in August 2019. The new processor Ascend 910 shows lower power consumption of 310w, which is much lower than the expected 350w. Microsoft has also entered the AI race with the launch of Graphcore’s AI chipsets. Both the companies, Microsoft and Graphcore have developed Intelligence Processing Unit (IPU). The Integration of IPU with Microsoft Azure will boost the performance of artificial intelligence applications. These IPUs have the capability to process 1,00,000 machine learning programs parallelly.

The adoption of AI applications has been growing across various industries. One industry that has huge demand for AI applications is the smartphone industry. This is due to the rise in demand for AI processors for smartphones. Apple developed AI processors for their iphone with its A11 Bionic SoC in the year 2017. The processor was specifically designed to support machine learning algorithms. In the year 2019, the company has developed A13 Bionic SoC, which is a combination of both CPU and GPU, along with its proprietary Neural Engine. Huawei, with the launch of its Kirin 990 processor in the same year, further raises the competition with Apple in the AI processor market. Apple and Huawei were the first OEMs to introduce a dedicated AI processor. Other companies such as Qualcomm, Samsung, MediaTek, ARM, etc., have also introduced AI processors, which is further contributing to the growth of the AI hardware market.

Figure 2: Artificial Intelligence (AI) Hardware Ecosystem: Segmentation

Components Product Technology End-User Micro Processor Smartphone Predictive Analytics Consumer Electronics ASIC Wearable Machine learning Automotive CPU Workstation systems Deep learning IT and Telecom FPGA Imaging Systems NLP Retail GPU Others Computer Vision Others Others Predictive Analytics Others

In the automotive industry, the idea of autonomous driving and the development of ADAS systems have created a huge demand for AI hardware. Google, Nvidia, Intel, Qualcomm, Xilinx, Tesla, Samsung, and Texas instruments are a few market players that are focusing on introducing more innovation in the automotive AI hardware Ecosystem. In 2019, Nvidia has announced its brand-new Orin AI SoC. The processor will consist of more than 17 billion transistors and will be smaller than its previous predecessor Nvidia’s Xavier. The production of Orin is expected to begin from the year 2022.

In 2019, Horizon Robotics has launched its 2nd generation automotive AI processor that meets the standards of Euro NCAP 2022. Samsung’s latest Exynos Auto V9 has the capacity to process six displays and twelve cameras simultaneously. Microsoft has upgraded its cloud platform Azure to meet the demand of level 3 autonomous system, as per the requirement of Audi. In 2019, Tesla has developed two AI chips that is 21 times faster than the older Nvidia models that were used by the company. These factors are increasing the demand for AI hardware.

Global AI Hardware Ecosystem:

As of 2019, the US is dominating the artificial intelligence (AI) hardware market. Most of the semiconductors sold worldwide belong to the companies based on US, and the number of companies designing AI chipset is more in the country. Nvidia, Intel, Microsoft, Apple, etc., are a few notable companies. These companies either manufacture AI hardware or invest in other start-ups to meet the demand across various industries. In China, the adoption of AI application is higher, and the country has emerged as the perfect competitor with the US.

At present, GPUs are dominating the AI hardware market and Nvidia is one of the leading players in GPUs, followed by Intel, AMD, and others. Most of the developed economies like the US and the UK, and developing economies like China, India, and other countries are focusing on digital transformation, which includes AI projects such as Smart city, Intelligent transportation, Public record maintenance, Tax audit, Law enforcement, etc. The development of new technologies such as AIoT and 5G will boost the demand in the AI hardware market during the forecasted time period.

Figure 3 – Market Statistics Glimpse: AI Hardware Ecosystem

There are many trends that are having an impact on the market forecast. These, when evaluated from a company’s perspective, can drive growth. Our numerous consulting projects have generated sizeable synergies across all regions and all sizes of companies.

The major players operating in the Global AI Hardware Ecosystem are as follows:

Company Ecosystem Positioning Total Revenue (US$) Industry Region Nvidia Corporation Hardware Manufacturers $ 11.7 Billion Semiconductors Global Intel Corporation Hardware Manufacturers $70.8 Billion Semiconductors Global Qualcomm Inc. Hardware Manufacturers $22.7 Billion Semiconductors Global Samsung Electronics Co., Ltd Hardware Manufacturers $210 Billion Consumer Electronics and Semiconductors Global Xilinx, Inc. Hardware Manufacturers $2.5 Billion Semiconductors Global

Very few markets have the interconnectivity with other markets like AI. Our Interconnectivity module focuses on the key nodes of heterogenous markets in detail. Processors, Smartphone, Cloud Computing, and Automotive markets are some of our key researched markets.

Figure 4 – Artificial Intelligence (AI) Hardware Major Interconnectivities

A Glance on the AI Hardware Ecosystem Trends:

Trends Components Products Industry vertical Technology Impact on Market There has been an increase in use of artificial intelligence-powered wearable for applications such as pregnancy monitoring and management, in order to detect signals including maternal heart rate, fetal heart rate, uterine activity, and others. Wearable 0.34% Processors are developed natively with more security and encryption, especially for ADAS applications. FPGA Automotive 0.68% Increasing use of voice-based commands in the Next Generation Smartphone industry will fuel the growth of NLP. Consumer Electronics NLP 0.50% Trend 4 XX XX Trend 5 XX XX

Research methodology: