Artificial Intelligence Computing Software Market 2022 Analysis Report: Comprehensive insights into AI-related processor specifications and capabilities by key market players and start-ups. –

DUBLIN–(BUSINESS WIRE)–The “Artificial Intelligence Software: Market Analysis” report has been added to from 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 research contains comprehensive information about the specifications and capabilities of AI-related processors that have been produced by major market players and start-ups.

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

After the major breakthrough at the turn of the century, AI began to integrate more and more artificial neural networks, connected in an ever-increasing number of layers, now known as Deep Learning (DL). They can compete with and outperform classic ML techniques such as clustering, but are more flexible and can work with much more complex datasets, including images and audio.

As machine learning entered exponential growth, it expanded into areas typically dominated by high-performance computing, such as protein folding and multi-particle interactions. At the same time, our lives are 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 new solutions and technologies, such as space exploration or fundamental physics.

More so, the commercial success of AI-based systems (autopilots, image processing, voice recognition and translation, to name a few) ensures that no shortage of funds can hinder this growth. It has clearly become a new industry, if not a sector of the economy, which is growing in importance year after year.

Like any industry, it depends on several factors to thrive. Growing consumer demand has led to the consensus of leading forecasters that the industry will grow rapidly – around 40% per year in the near future, so lack of funds is not an issue. Instead, we need to focus on other requirements for the efficient operation of the industry.

The three main components are the availability of processing tools, the abundance of raw materials and the workforce. In this case, the raw materials are represented by big data, and there are often more of them than our current systems can understand. The workforce also seems to be growing fast enough as ML cements its place in the college curriculum. Thus, processing tools, as well as the energy available to operate them, are obvious bottlenecks in exponential growth.

The end of Moore’s Law of Extrapolation due to quantum tunneling and the like, which are becoming increasingly important as transistors shrink, sets clear limits on what we can do. To ensure long-term investments in the industry, a clear strategy must be developed to compensate for what will happen in 10 years


  • Most DL-related tasks are performed on GPUs and ASICs. The main training workflow will still be GPU-related, but the increased adoption of AI in the consumer and peripheral segments will move the ratio towards parity, where 80% of the current market is dominated by GPUs.

  • The ASIC market has historically been much more varied than the CPU or GPU markets. Where there is a need that cannot be met by other means – there is an ASIC for that. Market players with large data centers are trying to optimize and grow their clouds while edge players are looking to extract every TOP from every watt. We expect the market for ASICs to grow much faster than for GPUs, with FPGAs becoming increasingly important in this space.

  • FPGAs were once a somewhat exotic part, taking the niche segments from the scientific and industrial sectors. The increase in AI-related demand and market integration has enabled rapid progress in the field and greatly expanded the capabilities of the FPGA.

  • We’re poised to see an average growth of 34% in the edge industry through 2025, as enterprises strive to reduce latencies associated with data transfer between data acquisition devices and centers of data processing. About 94% of Industrial Internet of Things (IIoT) and Robotic Process Automation (RPA) companies have already announced their intention to integrate cutting-edge AI or are already doing so. One of the growth factors in the Edge market is mobile processors. This sector is expected to almost double until 2025, from $13 billion in 2020 to $22 billion with an average annual growth of 10.7%.

  • Neuromorphic chips are clearly in the research and development phase, but the promise of ultra-low power consumption puts this kind of endeavor at the center of the industry’s long-term growth.

Main topics covered:

1. Deep Learning Challenges

1.1 Architectural limits

1.2 Brief introduction to deep learning

1.3 Take shortcuts

1.4 Processing tools

2. Market analysis

2.1 Market Overview

2.2 Processor

  • Intel

  • IBM

  • ARMS

  • Wave Computing

  • Amazon (Amazon Web Services)

  • Alibaba Group (T-Head Semiconductor Co.)

  • AMD (advanced microdevices)

  • NVIDIA 32 Huawei (HiSilicon Technologies)

  • Tachyum

2.3 Edge and Mobile

  • ARMS


  • Qualcomm

  • Samsung

  • Apple

  • You’re here

  • MediaTek

  • Intel (Mobileye)

  • Huawei (HiSilicon Technologies)

  • Kneron

  • Unisoc

  • Syntiant

  • Google

2.4 GPUs


  • Intel (Altera)

  • AMD (Xilinx)

2.6 ASICs

2.6.1 Tech giants

  • Intel

  • Amazon

  • Google (Alphabet)

  • Alibaba Group (T-head)

  • You’re here

  • Huawei

  • Qualcomm

  • Baidu (Kunlun Technologies)

2.6.2 Starts

  • Sophon.AI (Bitmain Technologies)

  • Graphcore

  • Groq

  • SambaNova Systems

  • Mythical

  • brains

  • Esperanto technology

  • Cambricon Technologies

  • Rebellions

  • EdgeCortix

2.7 Neuromorphic processors

  • Intel

  • brain chip

  • IBM

  • SynSense

2.8 Photonic computing

  • Light material

  • Light on

  • Lightelligence

  • Optalysys

3. Glossary

  • Artificial intelligence

  • Processor types

  • Edge vs Datacenter/Cloud

  • Systems

  • Architecture

  • Memory

  • Precision

  • technical parameters

  • Companies

4. Infographics

  • Market capitalization of public companies

  • Total financing of private companies

  • Fabrics

  • Processor Landscape

  • Performance evaluation of the FP16 calculation

  • Performance per watt of the FP16 calculation

  • Performance per watt of the FP32 calculation

  • Headquarters geography

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