AI Semiconductor

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AI Semiconductor

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Artificial Intelligence (AI) Semiconductor

It was the development of hardware, centered around the GPU, that enabled the development of DNN over its theoretical basis to application. Without the massive calculation capabilities of GPU, the development of artificial intelligence would have been very slow.

How AI is used in semiconductor industry?
AI plays a double role in the semiconductor industry: it acts as a key leverage element for digitising the manufacturing processes and provides the technology for semiconductor manufacturing to optimise the operations and control the process parameters as the technologies advance toward nanometre-scale semiconductor ...

Does AI use semiconductor?
Semiconductor architectural improvements are needed to address data use in AI-integrated circuits. Improvements in semiconductor design for AI will be less about improving overall performance and more about speeding the movement of data in and out of memory with increased power and more efficient memory systems.

Artificial intelligence is a concept in computer science in which machines “think” for themselves. No one knows exactly where or when the concept was started, but formal research into this idea generally is attributed to a 1956 project at Dartmouth College.

Artificial intelligence achieved some notoriety following the Arthur C. Clarke science fiction novel 2001: A Space Odyssey, which was published in 1968. In the book, a computer named HAL (one letter off from the IBM acronym) attempted to take over a space ship.

By the mid-1980s, artificial intelligence was considered the next big thing in computing. Every major computer maker had an AI research project, most of which were abandoned in the early 1990s due to insufficient processor speed, the high cost of memory, and slow networking speeds.

Moore’s Law and vast improvements in data movement have eliminated those bottlenecks, and AI has returned, both as a general category for development and in various subsets such as deep learning and machine learning. An artificially intelligent machine must utilize machine-learning algorithms to make choices based upon previous experience and data. The terms are often confusing, in part because they are blanket terms that cover a lot of ground, and in part because the terminology is evolving with technology. But no matter how those arguments progress, machine learning is critical to AI and deep learning.

AI, Semiconductors, and the Importance of Technology Education for Policymakers

By Melissa K. Griffith & Donald F. McLellan on May 31, 2022

In the United States—whether you are sitting in policy, industry, think tanks, or academia—there is an underlying concern, which is not always explicitly voiced, that we are not adequately capturing the full potential of emerging technology—capitalizing on the benefits and mitigating the risks—given the current geopolitical moment.

If we frame a goal of policy as the allocation or (re)allocation of resources that society or business would not have ready access to absent intervention, then keeping policy makers informed of significant developing trends and where policy intervention can most help right now is imperative.  It is also imperative to discuss how to avoid policy measures that would lead to unintended or counterproductive consequences.  For technology policy to be effective (whether to make us more innovative, more competitive, more equitable, more secure, more sustainable, or more resilient), policymakers need working familiarity with emerging technology trends from a trusted, objective resource that draws from relevant industry and academic communities of practice.  This education and community building effort needs to be sustained in a reliable and trusted way—that is exactly what the Wilson Center’s Technology Labs and Masterclasses were created to address.

Science and Technology Education for Policymakers

The Wilson Center's Technology Labs provide participants with a foundation for core science and technology policy topics through six-week seminar series focused on three core subject areas: (i) Artificial Intelligence (AI), (ii) Cybersecurity, and (iii) Digital Assets. Alums of these Technology Labs, alongside alums of the Wilson Center’s Foreign Policy Fellowship Program, also have access to a series of Technology Masterclasses throughout the year. These stand-alone seminars offer deeper dives into a range of topics including semiconductors, 5G networks, critical infrastructure protection, AI safety, and the evolution of space-based assets. Each Technology Lab and Masterclass session is led by top technologists and scholars drawn from the private, public, and non-profit sectors to provide mid-to senior-level Congressional and Executive Branch staff with a free peak behind the technology curtain.

Policy at the Intersection between AI and Semis

What does this effort look like in practice?  Over the course of a recent two-part Masterclass series, the Wilson Center’s Science and Technology Innovation Program (STIP) tackled the semiconductor industry’s role in driving AI innovation. This Masterclass series offered policy participants an opportunity to ask questions of leading experts in these fields in a closed door, not for public consumption environment – allowing the free flow of ideas.  In return industry participants had a chance to learn more about pressing policy concerns directly relevant to their work. Session One, AI and the Semiconductor Supply Chain, explored how AI is being and can be used in practice in the semiconductor industry itself from design to packaging. Session Two, The AI Chips of Today and Tomorrow, explored the importance and evolution of semiconductors for AI innovation and applications more broadly including vast implications of further AI developments for all.

This market research report was originally published at Woodside Capital Partners’ website. It is reprinted here with the permission of Woodside Capital Partners.

Palo Alto – July 29, 2022 – Woodside Capital Partners (WCP) is pleased to share our Industry Report on the AI Semiconductor Market 1st Half 2022, authored by Managing Director Shusaku Sumida.

This report covers the following aspects of the Semiconductor Market for the 1st half of this year:

  • VC Funding Activity and Trends
  • Significant Investments
  • Thoughts on SPAC
  • Notable Industry Events
  • Investments by Country
  • 2022 2nd Half Expectations
  • 147 Startups by Tech Type

Investment Activities in AI Semiconductor Startups in the 1st Half of 2022

Investment in AI semiconductors in 2021 hit a record high; $7.1B was invested in 70 companies (half of startups) in 93 transactions in the world. One reason is that $2.55B, 36% of the whole investment was made in one startup, Horizon Robotics in China, which focuses on Automotive Tech, in Q1 and Q2.

63% of the transactions were made in Edge AI. Investment in Cloud AI is also strong, with almost $1B invested in SambaNova and Groq.

1H of 2022, the investment in global startups was significantly reduced to only $881M with 47 transactions. Many transactions made in China did not disclose the invested amount. Since the number of transactions is at the same level as 2021, we guess probably $1.5B or more was invested. Also, we think the investment is becoming more selective.

AI semiconductor holds the future of semiconductor industry

Artificial intelligence (AI), once a futuristic concept, has now penetrated deep into our daily life. From home appliances to smart devices, Internet, healthcare, and autonomous driving, the list of AI applications influencing our industry and lifestyle cannot be exhaustive. As the AI technology expands its coverage, semiconductors, notably AI semiconductors are also undergoing dramatic changes. Not only traditional suppliers entrenched in the existing semiconductor industry but also other global big-tech companies are turning up the heat for AI chip development programs by investing astronomical sums and engaging in a variety of M&A deals. Following is a list of questions and answers on AI semiconductor, expected to be a pillar of the future semiconductor industry.

Who makes AI semiconductors and how big is the AI semiconductor market?

As AI has emerged as a game changer for the future IT industry, the AI semiconductor market is riding on the boom. To secure vantage points in the AI semiconductor market, not only traditional chipmakers including Qualcomm, Intel, and NVIDIA but also global big-tech companies such as SK telecom, Google, Amazon, Apple and Tesla are jumping on the AI chip bandwagon. Announcing a transition toward an AI service company, SK telecom is spearheading the AI semiconductor industry after commercializing its AI chip SAPEON X220 in 2020.

Some other big-tech companies are also considering the path of developing proprietary AI chips from the ground up as it is more advisable to develop AI semiconductors specific to their own service offerings. For example, Tesla is developing proprietary AI chops specific to autonomous driving applications.

Market research firm Gartner projected that the AI semiconductor market will grow to 34.3 billion dollars (approximately 40 trillion won) by 2023, and account for 31.3% of the entire system semiconductor market by 2030.

Among the semiconductors used for AI applications, highly versatile CPU and GPU technologies have already entered the maturing state and the market growth is now being driven by optimized low-power, high-efficiency ASIC solutions. Market demand for AI chips is expected to come not only from high-performance servers used in data centers but also from devices such as automobiles and smartphones, and shift from learning applications to inference systems. Initially, demand for learning applications such as machine learning algorithms will be significant, but inference chips implementing AI services based on learning data are expected to drive the market growth in the long run.