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Semiconductors

Unleashing the full potential of AI

November 21, 2022 5-MINUTE READ

RESEARCH REPORT

In brief

  • AI hardware and the semiconductor industry are in a prime position to be a key driver for future AI advancements.
  • We’ve identified 3 major trends driving the evolution of AI in the semiconductor industry.
  • The demand and supply gap in AI compute requires the industry stakeholders to explore new business model together.

Artificial Intelligence is transforming the way that the world lives, works, plays and nearly every industry. Most AI development has been limited to the amount of data and computing bandwidth needed to train the AI model, while that’s been adequate for many functions, the potential is far greater to evolve every aspect of our lives.  

The key to unlocking the full potential is semiconductors. The manufacturing opportunities play a leading role in driving the next wave of computing and economic innovation.

"As Artificial Intelligence becomes more ubiquitous, hardware is playing a more significant role in the advancement of AI. This is a huge opportunity for the semiconductor industry, but also comes with pains that the industry needs to address."

— Shungo Saito, Director – Program Development, Global Semiconductor Alliance (GSA)​

There are three key observations in the semiconductor industry that are driving the evolution of AI across two critical components.  These components will challenge the industry into rethinking the way we operate and collaborate its monetization strategy, energy consumption and the trajectory of AI technology evolution and scalability.

Major trends driving AI evolution

Training and inferencing are critical components of AI applications that are driving these trends in the semiconductor industry.

01.

While software drove the initial burst of advancements in AI, the parallelism of hardware and software development will achieve the next major AI breakthroughs.

02.

Although the growth in AI creates tremendous demand for semiconductors and puts them in a position of power, the industry will continue to experience growing pains.

03.

Demand for compute power far outstrips its supply and the industry’s current business model is not well suited to address this imbalance.

When it comes to addressing demanding AI applications, there are two options to consider on either having the compute and data storage within the same chip or using off-chip memory that is specifically designed to handle high bandwidth and high capacity modeling.

The semiconductor industry has been advancing Moore’s Law for decades, despite predicting its death many times.  As we look to the future of AI, there are various options for meeting the exponentially increasing compute power that will be required.

01.

New devices and materials such as carbon nanotubes, graphene and spintronics.

02.

New architectures and packaging such as 3D packaging, optical interconnects, and reconfigurable computing.

AI is at the forefront in changing the world, the way we live and how people perceive what is possible. As the implementation of AI becomes more targeted towards end use cases and the divergence of AI training and inference continues, the interlock between hardware and software development needs to be more intimate. 

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"The larger the neural network, the better the results. But, at the cost of energy. We relentlessly improved energy efficiency of ML platforms and in some cases yielded orders-of-magnitude, with no signs of the slowing down."

— Yanamadala Chowdary

About the Authors

Timothy Chu

Managing Director – Strategy & Consulting, Semiconductor

Michael Kurniawan

Manager – Strategy & Consulting, Semiconductor

Jaya Shukla

Senior Manager – Industry Consulting, High Tech

Yanamadala Chowdary

Technology Strategy Business leader, ARM

Shungo Saito

Director – Program Development, Global Semiconductor Alliance (GSA)​

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