Top 20 AI Chip Companies Revealed [2023]


AI chips, the desire for more effective and potent hardware solutions has increased due to the quick development of artificial intelligence (AI) technology. AI chips, which are often referred to as AI accelerators or neural processing units (NPUs), are specialized processors made to carry out AI-related activities at previously unheard-of speeds and with extreme energy efficiency.

In this article, we’ll look at some of the leading manufacturers of AI chips that are influencing the development of AI-driven software and leading this cutting-edge sector.

Top AI Chip Companies


The parent company of Google is in charge of creating artificial intelligence technologies for some sectors, including cloud computing, data centres, mobile devices, and desktop PCs. The most famous component is undoubtedly the Tensor Processing Unit, an ASIC designed specifically for Google’s TensorFlow programming framework, which is mostly used for machine learning and deep learning, two areas of AI.

The Edge TPU is made for “edge” devices, which are things that are at the edge of a network and include things like smartphones, tablets, and other things that the rest of us use outside of data centres. The Edge TPU is much smaller than Google’s Cloud TPU, a data centre or cloud solution that is around the size of a credit card.


Since Apple has long made its own semiconductors, it’s possible that it will eventually stop depending on companies like Intel. If so, this would represent a substantial shift in strategy. Apple, which previously effectively broke relations with Qualcomm after a long legal struggle, is eager to create its own way in the realm of artificial intelligence.

The A11 and A12 “Bionic” CPUs are found in the company’s most current iPhones and iPads. The A12 Bionic processor is said to be 15% quicker than the previous generation while using 50% less power. The chip uses Apple’s Neural Engine, a part of the circuitry that other developers’ apps cannot access.


Arm, or ARM Holdings, developed the semiconductor designs that are used by all of the major IT firms, including Apple. Similar to how Microsoft profited from not manufacturing its own computers, it has an edge over rivals since it is a semiconductor designer rather than a chip producer. In other words, Arm has considerable market influence.

Project Trillium, a new family of scalable, “ultra-efficient” processors targeted at Machine Learning Processor, and Arm NN, a processor designed to work with TensorFlow, Caffe, a deep learning framework, and The three major tracks that the business is now researching AI chip designs along are additional architectures.


The top chipmaker in the world was allegedly earning $1 billion from the sale of AI processors as early as 2017. Despite not being the largest chip manufacturer now, Intel was back then. The Xeon line of processors, which is more of a general processor that has been upgraded than a processor that is specifically meant to handle AI, were the ones being considered in that report.

In addition to perhaps enhancing Xeon, Intel has developed a range of AI chips known as “Nervana,” sometimes known as “neural network processors.”


As we previously stated, Nvidia looks to be leading the market for GPUs, which can process AI tasks more quickly than general-purpose processors. In a similar vein, it appears that the company has gained a competitive edge in the growing market for AI processors. The advancements in GPU technology made by Nvidia appear to be closely related to the AI processor, hastening its creation.

In actuality, Nvidia chipsets might be regarded as AI accelerators, and its AI products seem to be backed by GPUs. The Tesla chipset, Volta, and Xavier are just a few of the several AI chip technologies that Nvidia supplies to the market. The software-plus-hardware bundles that contain all of these GPU-based chipsets are tailored to meet specific requirements.

Advanced Micro Devices (AMD)

Similar to Nvidia, AMD is a semiconductor manufacturer with a close relationship to graphics cards and GPUs, in part due to the recent growth of Bitcoin mining and the expansion of the computer games industry.

AMD offers hardware-and-software solutions for deep learning and machine learning, such as EPYC CPUs and Radeon Instinct GPUs. Epyc is the name of the graphics processor AMD offers for servers, particularly in data centres, whereas Radeon is a graphics processor primarily targeted at gamers. Additional AMD CPUs include the Ryzen and, perhaps, the Athlon, which is more well-known.


Baidu is the Chinese version of Google since it is widely used as a web search engine. Baidu has also made a splash in innovative and intriguing business areas like driverless cars, which call for powerful CPUs and AI chips. And to do this, last year, Baidu unveiled the Kun Lun, which it referred to as a “cloud-to-edge AI chip.”


After discussing seven well-known organizations whose main activities are not actually focused on developing AI chips, we arrive at Graphcore, a new company whose major mission is to develop and supply AI chips to the market. The company’s Colossus-based Rackscale IPU-Pod, aimed at data centres, appears to be its main offering at the time.

However, given that that is where its future resides, it may grow more with the imaginary amount of money paid. The business, which is currently valued at over $2 billion, has persuaded organizations like BMW, Microsoft, and other well-known brands to invest a total of $300 million in it.


Since the start of the smartphone boom, Apple has been a big source of revenue for Qualcomm; therefore, the tech giant’s decision to cease buying its chips leaves Qualcomm feeling abandoned. On the other hand, Qualcomm has made some big investments with the long term in mind and is, of course, no unknown in its sector.

According to analysts, Qualcomm came to the market for AI processors a little bit late. Even so, the company’s depth of understanding of the mobile industry would be helpful in achieving Qualcomm’s stated objective of “making on-device AI ubiquitous.”


One of the most intriguing companies on this list is Parallella, which is frequently referred to as the most affordable supercomputing machine available. The Epiphany, a 1024-core 64-bit microprocessor promoted as a “world first,” is Adapteva’s main AI chip product. The Darpa-funded company successfully ran a Kickstarter campaign for its Parallella product, raising more than $10 million in total investment.

Mystical AI

With more than $40 million in investment, Mythic plans to implement its “AI Without Borders” ideology throughout the globe, starting with data centres. The company says that its system does combined digital and analog computations inside flash arrays, which is a “completely new methodology,” and that it has discovered a way whereby deep neural networks no longer heavily impact traditional local AI. It can do “huge parallel processing” while seemingly floating in midair because to its modest size and desktop computer-speed GPU.


Samsung, which has eclipsed Intel as the world’s largest chipmaker and Apple as the top smartphone manufacturer, is aiming to enter hitherto unexplored markets. The most recent Exynos CPU from Samsung, which is made for long-term evolution, or LTE, communications networks, was released just before the previous year concluded. The number of on-device neural processing units has grown, according to Samsung’s latest Exynos.

The Company that Manufactures Semiconductors in Taiwan

TSMC is not exactly a boastful company, despite being one of Apple’s main suppliers of semiconductors for a long time. Despite having a website and updating investors on its findings, it doesn’t talk much about its actual work. Thankfully, news sources like DigiTimes keep up with developments at the chipmaker and recently reported that e-commerce giant Alibaba has contracted TSMC and Global Unichip to build an AI chip.


The semiconductor division of Huawei is this. Several indirect trade embargoes presently target Huawei, a manufacturer of telecom equipment. The US no longer permits Huawei to do business there, and other European countries are also copying the US. HiSilicon’s AI chip technology is probably still in its infancy in any case. To combat the growing number of supply constraints that Huawei is subject to, the business will need to intensify its efforts.


No such list would be complete if IBM wasn’t included at least once. As you might expect, IBM has made significant investments in the research and development of several AI-related technologies. Despite using normal processors rather than AI-specific ones, the company’s much-discussed Watson AI is nonetheless reliable. TrueNorth from IBM most likely falls within the category of specialised AI processors. Before you realise that Epyc has 19.2 billion transistors, the enormous 5.4 billion transistors in AMD’s TrueNorth, a “neuromorphic chip” designed to resemble the human brain, may look like a lot.


Xilinx produces microprocessors that have the most transistors in terms of parts per unit. The Versal or Everest chipsets are said to feature 50 billion transistors. Xilinx does in fact refer to Versal as an AI inference platform. The conclusions that machine learning and deep learning systems make from the massive amounts of data they consume and analyse are referred to as “inferences.” Chips produced by other companies are a part of the whole Versal and Everest systems. However, Xilinx is probably among the first to offer such high-power computing capabilities in standalone packages to the market.


Via does give what it terms an “Edge AI Developer Kit,” which contains a Qualcomm CPU and numerous other components, even if it does not offer an AI chip per se. It also enables us to mention another category of business. It’s probably just a matter of time before all the other makers of inexpensive, little computers, like Arduino and Raspberry Pi, implement AI. A couple of them already have an AI chip. According to Geek, Pine64 is one of them.


A behemoth that looks to be agile, LG is one of the biggest producers of consumer electronics. The fact that it is interested in robots is evidence of this, but many companies are also getting ready for the time when more intelligent machinery will be possible thanks to smart houses. This website earlier reported that LG has debuted its own proprietary AI processor, the LG Neural Engine. The company claims that this action is a component of a plan to “accelerate the development of AI gadgets for the home.” Even before the chips reach the edge devices, LG may still employ them in its data centres and back-end systems.

Technologies Imagination

Virtual and augmented reality applications demand the most computing power to operate. A few years ago, when the augmented reality game Pokémon was a global sensation, some of Google’s data centre servers purportedly ground to a standstill. As a result, it is unquestionably required for VR and AR to integrate AI processors in the data centre and the edge device. Imagination sort of manages that as well with its PowerVR GPU.


This company has more than $200 million in investment, which gives it the means to design distinctive AI chips for its customers. Even though the company is still in its early stages, SambaNova asserts that it is creating hardware-plus-software solutions to “drive the next generation of AI computing.” One of the key investors in the company is Alphabet or Google. You’ll observe that several well-known, major firms are funding innovative, brand-new startups in an effort to preserve them from competition.


A small number of former Google employees, including one or two who worked on the Tensor project, created this firm, which is allegedly quiet. The company’s beliefs are based on the claim that the next “breakthrough in computation will be fueled by new, streamlined architectural approach to hardware and software,” as the firm puts it, and Crunchbase disclosed that the business had raised $60 million to develop its theories last year.

Earlier articles on this company appeared in Kalray Robotics and Automation News. One of its top executives who gave us a presentation is featured in an interview on our YouTube channel. In essence, Kalray is a wealthy European business that appears to have created a state-of-the-art semiconductor for AI processing in data centres and on-edge gadgets. The company claims that its method allows several neural network layers to compute simultaneously while using very little electricity.


It makes sense for Amazon to enter the AI chip market given that it effectively created the cloud computing industry with its Amazon Web Services business unit, and especially considering the likelihood that their integration will increase the efficiency of Amazon’s data centres. The world’s largest online store made an announcement about its AWS Inferential AI processor at the end of the previous year. Even after its formal launch, it is unlikely to be offered to other businesses; rather, it will only be made available to those that are part of the Amazon group of companies.

Brain Systems

Cerebras Systems was founded in 2015. The business unveiled Cerebras WSE-2 in April 2021, an AI chip model with 850,000 cores and 2.6 trillion transistors. Without a doubt, the WSE-2 performs better than the WSE-1, which has 1.2 trillion transistors and 400,000 computing cores. Numerous pharmaceutical firms, including AstraZeneca and GlaxoSmithKline, employ Celebra’s technique because the WSE-1 technology performs so effectively and expedites genetic and genomic research.

Hailo AI

Hailo, an Israeli chipmaker with an emphasis on artificial intelligence, has developed a tailored Artificial Intelligence (AI) processor that provides edge devices with the performance of a data centre-class computer. In order to enable smart devices to perform sophisticated deep learning tasks like object identification and segmentation in real-time with the least amount of power, space, and money, Hailo’s AI processor completely reimagines conventional computer architecture.

A wide range of industries, including automotive, industry 4.0, smart cities, smart homes, and retail, will be impacted by the deep learning processor’s ability to integrate with various intelligent machines and devices. It supports its high-performance Hailo-8TM M.2 and Mini PCIe AI acceleration modules.

AI Anari

Anari AI is building the AI hardware industry from scratch by providing a novel method for AI chip design and application. With just one click, customers can swiftly build, deploy, and customise their own solutions and infrastructure thanks to its reconfigurable AI innovation. On 3D/Graph data structures, Anari’s ThorX processor, the first on the Anari platform, delivers 100x more computational efficiency than GPU.

An Overview of the Chinese Industry for AI Chip Design

Chinese initiates to create homegrown or independent artificial intelligence (AI) technology will be examined in this article. We will focus on the rivalry between Chinese and international businesses as well as the effects of a technological war on China’s AI chip development sector. We will also draw attention to the potential and difficulties that local Chinese businesses have when competing with major international competitors. Because of this, it might be difficult to excel in this field.

Chinese Ambitions to create homegrown or independent artificial intelligence technology. Even if China is lagging behind many other countries in the use of AI, there are still certain important areas that require attention. The nation must first refocus its educational system on innovation and digital capabilities. Second, it has to create an immigration plan that draws in the best talent from around the world. The deployment of AI within China’s conventional sectors must be encouraged, as the third step.

Chinese businesses struggle with issues such as a lack of financial resources, technological expertise, and strategic understanding. By establishing fiscal incentives and leading the deployment of AI within the government, the Chinese government should remove these impediments. Supply networks, manufacturing footprints, and end-to-end value chains will all change as a result of AI applications. To shorten the development cycle, increase engineering effectiveness, avoid errors, and enhance safety, they will exploit the rapid expansion in data volume.

By lowering inventory costs, enhancing supply and demand forecasting, and identifying sales leads, artificial intelligence may help businesses save money. While increasing production, it can assist organizations in cost reduction. Applications of AI can also increase productivity, reduce costs, and improve the effectiveness of manufacturing operations.

Best Indian Stocks

Now that we have a better understanding of this sector, let’s evaluate the best artificial intelligence stocks in India.

S.No.Company Name
1.Tata Elxsi Ltd.
2.Bosch Ltd.
3.Kellton Tech Solutions Ltd.
4.Happiest Minds Technologies Ltd.
5.Zensar Technologies Ltd.
6.Persistent Systems Ltd.
7.Saksoft Ltd.
8.Oracle Financial Services Software Ltd.
9.Affle India Ltd.
10.Cyient Ltd.

Stock Market For AI Chip Companies

With a predicted CAGR of 37.1% from 2022 to 2031, the market for artificial intelligence chips worldwide, which was valued at $11.2 billion in 2021, is expected to reach $263.6 billion by 2031.

The artificial intelligence chip market is anticipated to have impressive growth throughout the projected period of 2022–2031, according to Himanshu Jangra, Lead Analyst, Semiconductor and Electronics at Allied Market Research. The market size, Artificial intelligence chip market trends, significant industry players, sales analysis, key driving factors, and important investment pockets are all thoroughly examined in the research.

The market overview, definition, and scope are all included in the study on the worldwide artificial intelligence chip market. Market expansion is impacted by continuous technical developments as well as a rise in demand for artificial intelligence processors and brain chip solutions. Additionally, the research includes a discussion of the pain points, a value chain analysis, and important laws in addition to a quantitative and qualitative analysis of the artificial intelligence chip market opportunity.  

Specialized silicon chips called artificial intelligence (AI) chips are used for machine learning and contain AI technology. AI assists in lowering or eliminating the danger to human life in a number of commercial sectors. As the amount of data has expanded, it has become increasingly important to develop systems that are more effective at addressing mathematical and computational issues. As a result, the bulk of the big businesses in the IT sector concentrates on creating AI AI chips and applications.


Companies that make AI chips are essential to expanding the possibilities for applications of artificial intelligence. These businesses are driving the AI revolution and creating new opportunities in industries like healthcare, autonomous driving, finance, and others by creating specialized hardware solutions that are optimized for AI workloads. The rivalry among these top AI chip firms will encourage further innovation as the AI sector develops, ultimately helping both businesses and consumers.


What company makes chips for AI?

The semiconductor company AMD produces CPU, GPU, and AI accelerators. Alveo U50 data centre accelerator card from AMD, for instance, features 50 billion transistors. In milliseconds, the accelerator can run 10 million embedding datasets and execute graph algorithms.

Who has the most advanced AI chip?

In terms of processors used in artificial intelligence (AI) systems, Nvidia has grown to dominate the industry.

Which company makes the most AI chips?

Advanced AI chips are produced by the Taiwan Semiconductor Manufacturing Company (TSMC) across the world. This refers, first and foremost, to Nvidia’s GPUs; it also covers the AI processors from Google, AMD, Amazon, Microsoft, Cerebras, SambaNova, Untether, and every other respectable rival.

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