Artificial Intelligence Computing Software Market Analysis Report 2022: Complete Information on the Specifications and Capabilities of AI-related Processors by Major Market Players and Startups. – – | Region & Cash

DUBLIN–(BUSINESS WIRE)–Added the Artificial Intelligence Computing Software: Market Analysis report Offer.

The market is expected to grow from $6.9 billion in 2021 to $37.6 billion in 2026 and could become a new sector of the economy.

This study provides complete information on the specifications and capabilities of the AI-related processors made by the major market players and startups.

This comprehensive analysis can help you with your technology acquisitions or investment decisions related to the fast-growing AI processor market.

After the big breakthrough around the turn of the century, AI began to integrate more and more artificial neural networks connected in an ever-growing number of layers known today as deep learning (DL). They can compete with and outperform classic ML techniques like clustering, but are more flexible and can work with much more complex datasets, including images and audio.

With the exponential growth of machine learning, it expanded into areas normally dominated by high-performance computing—such as protein folding and many-body interactions. At the same time, our life is becoming increasingly dependent on its availability and reliability. This poses a number of new technical challenges, but at the same time opens the way to novel solutions and technologies, similar to what space exploration or fundamental physics do.

Additionally, the commercial success of AI-enabled systems (autopilot, image processing, speech recognition, and translation, to name a few) means that no lack of funds could hamper this growth. It has clearly become a new industry, if not a branch of the economy, which is growing in importance every year.

Like any industry, its success depends on several factors. Rising consumer demand has led to a consensus from the main forecasters that the sector will grow rapidly – around 40% annually in the near future, so shortage of funds is not an issue. Instead, we need to focus on other requirements for the efficient functioning of the industry.

The three main components are the availability of processing tools, the abundance of raw materials, and the workforce. Commodities in this case are represented by big data, and there is often more of it than our current systems can meaningfully capture. The workforce also appears to be growing sufficiently rapidly as ML consolidates its place in the university curriculum. So the processing tools, as well as the energy available to run them, are clear bottlenecks in exponential growth.

The end of Moore’s law of extrapolation due to quantum tunneling and the like becoming increasingly important as transistor sizes are reduced puts clear limits on where we can go. To ensure long-term investment in the industry, a clear strategy needs to be developed to offset what will happen in 10 years

Key Highlights

  • Most DL-related tasks are performed on GPUs and ASICs. The core training workflow will remain GPU-bound, but increasing adoption of AI in the consumer and edge segments will shift the ratio towards parity, while GPUs currently dominate 80% of the market.

  • The ASICs market has historically been much more diverse than the CPU or GPU markets. Where there is a need that cannot be answered any other way, there is an ASIC for it. The market players with large data centers are trying to optimize and scale their clouds, while edge players are trying to get every TOP out of every watt. We expect the ASIC market to grow much faster than GPUs, with FPGA gaining a foothold in this space.

  • FPGAs used to be rather exotic, occupying niche segments in science and industry. The rise of AI-related demand and market integration enabled the rapid progress in this field and dramatically expanded the capabilities of FPGA.

  • We’re poised to see an average 34% growth in the edge sector through 2025 as organizations strive to reduce the latency associated with data transfer between data collection devices and data centers. About 94% of Industrial Internet of Things (IIoT) and Robotic Process Automation (RPA) companies have already declared plans to integrate Edge AI or are already doing so. Mobile processors are one of the growth factors in the edge market. This sector is expected to nearly double by 2025, from $13 billion in 2020 to $22 billion with a compound annual growth of 10.7%.

  • Neuromorphic chips are clearly in the research and development phase, but the promise of ultra-low power consumption puts these types of trials at the heart of the industry’s long-term growth.

Main topics covered:

1. Deep Learning Challenges

1.1 Architectural Restrictions

1.2 Brief Introduction to Deep Learning

1.3 Cut corners

1.4 Processing Tools

2. Market Analysis

2.1 Market Overview

2.2 Processor

  • intel

  • IBM

  • POOR

  • WaveComputing

  • Amazon (Amazon Web Services)

  • Alibaba Group (T-Head Semiconductor Co.)

  • AMD (Advanced Micro Devices)

  • NVIDIA 32 Huawei (HiSilicon Technologies)

  • tachyum

2.3 Edge and Mobile

  • POOR

  • Nvidia

  • Qualcomm

  • Samsung

  • Apple

  • Tesla

  • MediaTek

  • Intel (Mobileye)

  • Huawei (HiSilicon Technologies)

  • Kneron

  • Unisoc

  • Synthetic

  • Google

2.4 GPU

2.5 FPGA

  • Intel (altera)

  • AMD (Xilinx)

2.6 ASIC

2.6.1 Tech giants

  • intel

  • Amazon

  • Google (alphabet)

  • Alibaba Group (T-Head)

  • Tesla

  • Huawei

  • Qualcomm

  • Baidu (Kunlun Technologies)

2.6.2 Startups

  • Sophon.AI (Bitmain Technologies)

  • graph core

  • groq

  • SambaNova systems

  • Mythical

  • cerebrum

  • Esperanto technology

  • Cambricon Technologies

  • rebellions

  • Edge Cortix

2.7 Neuromorphic Processors

  • intel

  • BrainChip

  • IBM

  • SynSense

2.8 Photonic Computing

  • light matter

  • light on

  • light property

  • Optalysys

3. Glossary

  • Artificial intelligence

  • processor types

  • Edge vs Data Center/Cloud

  • systems

  • Architecture

  • memory

  • precision

  • technical parameters

  • companies

4. Infographics

  • Market capitalization of listed companies

  • Total financing of private companies

  • fabrics

  • processor landscape

  • Performance Evaluation of Computing FP16

  • Performance per watt of the calculation of FP16

  • Performance per watt of the calculation of FP32

  • Headquarters geography

For more information about this report, visit

Leave a Comment