(MENAFN- Comserve) Shibuya-ku, Tokyo, Japan, Japan, Oct 07, 2021, 02:35 /Comserve / -- AI Computing Hardware Market With Top Countries Data, Industry Analysis , Size, Share, Revenue, Prominent Players, Developing Technologies, Tendencies and Forecast
The AI computing hardware market is expected to record a CAGR of 26% during the forecast period (2020-2025). More recently, the AI boom has sparked a stream of startup hardware companies developing more specialized chips, which are optimized for specific applications, such as autonomous driving and surveillance cameras. Graphcore and a few other players offer much more flexible chips, which are not only crucial for developing AI applications but also much more challenging to produce. In December 2019, Microsoft funded USD 200 million to Graphcore to find hardware that will make its cloud services more attractive to the growing number of customers for AI applications. If sustained, the increasing number of cloud services may help in growing the hardware market.
- The demand for AI computing hardware in the defense sector drives the market. The air force needs unconventional computing architectures for pattern recognition, event reasoning, decision making, adaptive learning, and autonomous tasking on energy-efficient manned and unmanned aircraft. As per researchers, the major focus area is neuromorphic computing or brain-inspired computing that involves processors more advanced than more-traditional the Von Neumann architectures. This kind of design could lead to unconventional circuits based on emerging nanotechnologies, like memristors and nano-photonics.
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Key Market Trends
Automotive Sector to Witness Significant Growth
- The automotive industry is going through a decade of rapid changes, as vehicles become more connected, new propulsion systems, such as electric motors, reach the mainstream, and the level of vehicle autonomy rises. Many car makers have already responded by announcing pilot projects in autonomous driving, which may need AI computing hardware.
- For instance, NVIDIA DRIVE AGX self-driving compute platforms are built on NVIDIA Xavier, the world's first processor designed for autonomous driving. The auto-grade Xavier system-on chip (SoC) is in production, and it is architected for safety, incorporating six different types of processors to run redundant and diverse algorithms for AI, sensor processing, mapping, and driving.
- Further, Xpeng P7 is the first L3 autonomy-ready production vehicle in the Chinese market, powered by NVIDIA's DRIVE AGX Xavier system-on-a- chip, delivering 30 TOPS (trillions of operations per second) performance while consuming only 30 Watts of power. Its autonomous driving system, XPILOT3.0, is made for China's challenging roads. It contains 12 ultrasonic sensors, five millimeter-wave radars, 14 cameras, and the industry's only 360° multi-perception integrated system.
- Further, in April 2020, the autonomous vehicle startup, Phantom AI, raised USD 22 million ina Series A financing led by Celeres Investments and joined by the US automaker, Ford Motor Co., and KT, South Korea's largest telecommunications company. Phantom AI focuses on including computer vision, sensor fusion, and control capabilities in its solutions and accelerate its production globally.
- Furthermore, players are focusing on the next generation of intelligent viewing platforms for surround-view visualization, driver monitoring stand-alone vision processing, and e-mirror solutions. In April 2020, Ambarella announced the CV22FS and CV2FS automotive camera SoCs, with AI processing and ASIL-B compliance, in order to enable safety-related applications.
Asia-Pacific to Register the Fastest Growth Rate
- Asia-Pacific is expected to register a significant growth rate due to advancements in AI technology in countries, such as China and Japan, where players are focused on integrating computing hardware in the devices through partnerships.
- In April 2020, the Chinese AI chip maker, Intellifusion, completed a pre-IPO round of financing of nearly CNY 1 billion (USD 141 million), led by Utrust VC, Forebright Capital, and its existing investor, Walden International. Intellifusion focuses on the field of visual intelligence. Its chip platform, Moss, recently launched the second-generation artificial intelligence chip, DeepEye1000, which is a heterogeneous multi-core visual analysis SoC with a custom instruction set neural network processor embedded.
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- The unit performance of DeepEye1000 increased by 20 times and the unit energy efficiency increased by 100 times, with system delay reducing by 200 times. It can be applied in the intelligent security, new business, intelligent transportation, intelligent manufacturing, intelligent storage, intelligent home, robot, intelligent supercomputing, and other industries. This further supports the market growth.
- In August 2019, Huawei announced Ascend 910, its AI processor for data training, and its AI computing framework, MindSpore. The processor delivers 256 TeraFLOPS for half-precision floating points (FP16) and 512 TeraFLOPS for integer precision calculations (INT8), respectively. Further, Huawei plans to develop Atlas and MDC products based on Ascend processors, which can be provided to universities and other partners in India, as they develop applications to address industry-specific challenges. This may further boost the market growth in the future in India and China.
Competitive Landscape
The AI computing hardware market is highly fragmented, and the major players have used various strategies, such as new product launches, agreements, joint ventures, partnerships, and acquisitions, to increase their footprints in this market. Key players are Cadence Design Systems Inc., Synopsys Inc., etc. Recent developments in the market include -
1 INTRODUCTION
1.1 Study Deliverables
1.2 Study Assumptions
1.3 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
4.1 Market Overview
4.2 Market Drivers
4.2.1 Demand for AI Computing Hardware in the Defense sector
4.2.2 Adoption of Field-programmable Gate Arrays (FPGA) for High Computing Speed
4.3 Market Restraints
4.3.1 Limited Number of AI Experts and High Power Consumption
4.4 Industry Value Chain Analysis
4.5 Industry Attractiveness - Porter's Five Forces Analysis
4.5.1 Threat of New Entrants
4.5.2 Bargaining Power of Buyers/Consumers
4.5.3 Bargaining Power of Suppliers
4.5.4 Threat of Substitute Products
4.5.5 Intensity of Competitive Rivalry
5 MARKET SEGMENTATION
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