Author:
Ravindra Bidnur

Over the last decades, one might have thought or perceived slowness in innovation in semiconductors. To some extent, it is true that software has been the star of high tech over the past few decades, and it’s easy to understand why. With PCs and mobile phones, the game-changing innovations that defined this era, the architecture and software layers of the technology stack enabled several important advances. The semiconductor companies were in a difficult position. Although their innovations in chip design and fabrication enabled next-generation devices, they received only a small share of the value coming from the technology stack. With new domains like Machine Learning (ML) and Artificial intelligence (AI) gaining popularity in almost all application domains, it has opened up new innovations in chip architecture and design. The story of the semiconductor industry is changing with the growth of AI – typically defined as the ability of a machine to perform cognitive functions associated with human minds, such as perceiving, reasoning, and learning. This blog captures some of the AI ideas and how it is changing the landscape of the semiconductor industry.

A brief overview on AI:

Artificial Intelligence or AI, in short, is a branch of computer science that displays or simulates Human intelligence by machines or a process to make machines think intelligently. AI is based on the study of how the human brain thinks, and how humans learn, decide, and work while trying to solve a problem. The great American computer scientist, also called the Father of AI, John McCarthy, first coined the term in 1956. In today’s world, this term encompasses everything from Robotics to Process automation.

The goal of AI is to implement Human intelligence in machines and to create smarter systems. Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major impetus of AI is in the amelioration of computer functions correlated with human intelligence, such as Problem-solving, Learning, and reasoning. AI is a multi-disciplinary domain, wherein there is an equal opportunity for every field to contribute.

AI techniques heighten the speed of execution of the complex program it is equipped with and which is normally not achievable by humans. Some of the applications or major advances in areas of AI are Significant demonstrations in machine learning, Case-based reasoning, Multi-agent planning and Scheduling, Gaming, Natural language processing (understanding and translation), Expert systems (examples involve Flight tracking system, clinical systems), Vision systems, speech and voice recognition, Intelligent robots, Data mining, Virtual Reality etc.

The biggest challenge for AI is Creativity which is a fundamental trait of human intelligence. AI techniques can be used to spawn innovative ideas, by generating innovative combinations of familiar ideas, exploring the potential of conceptual spaces, and making transformations that enable the generation of previously impossible ideas.

There is a large multitude of applications where AI is serving or integrated into human beings in their everyday lives with or without their realization, like Washing machines, dishwashers, cars we drive, Automatic doors, Smartphones, etc. to Autonomous vehicles, space robots, and the list is endless. Ai is playing a very advanced role in the medical field of diagnosis and help in early detection and warning of various medical ailments like heart attack, paralysis strokes, etc.

AI’s role in Semiconductor industry:

As one understands the AI’s goal, it becomes apparent how it can open-up opportunities in various business opportunities across various domain.

Diverse solutions, as well as other emerging AI applications, share one common feature: a reliance on hardware as a core enabler of innovation, especially for logic and memory functions. This leads to the following questions …

What will this development mean for semiconductor sales and revenues? And which chips will be most important to future innovations?

To answer these questions, it is important to reviewed current AI solutions and the technology that enables them. Also examined opportunities for semiconductor companies across the entire technology stack. The outcome of this study, can be concluded as

·AI could allow semiconductor companies to capture 40 to 50 percent of total value from the technology stack, representing the best opportunity they’ve had in decades.

· Storage will experience the highest growth, but semiconductor companies will capture most value in compute, memory, and networking

· To avoid mistakes that limited value capture in the past, semiconductor companies must undertake a new value-creation strategy that focuses on enabling customized, end-to-end solutions for specific industries, or “microverticals.”

· Innovate and enable multi-disciplinary domains coming together to define a end-to-end solutions.

By keeping these beliefs in mind, semiconductor leaders can create a new road map for winning in AI. We will look at opportunities to enable AI applications by taking an example below.

AI will drive a large portion of semiconductor revenues for data centers and the edge:

With hardware serving as a differentiator in AI, semiconductor companies will find greater demand for their existing chips, but they could also profit by developing novel technologies, such as workload-specific AI accelerators

DomainCurrentTrend for AI
Compute GPU’s and FPGA’sWorkload specific AI accelerators
Memory HBM’s On-chip SRAMsNew NVM (non-volatile Memories)
StorageData centers with increased capacityAI optimized data centers with enabled by NVM
NetworkingInfrastructure for data communicationProgrammable switched with high speed interconnects

Research revealed that AI-related semiconductors will see growth of about 18 percent annually over the next few years—five times greater than the rate for semiconductors used in non-AI applications. If this growth materializes as expected, semiconductor companies will be positioned to capture more value from the AI technology stack than they have obtained with previous innovations—about 40 to 50 percent of the total.

To conclude,

·       It’s clear that opportunities re plenty, but success isn’t guaranteed for semiconductor players. To capture the value they deserve, they’ll need to focus on end-to-end solutions for specific industries (also called microvertical solutions), ecosystem development, and innovation that goes far beyond improving compute, memory, and networking technologies.

·       Semiconductor companies must define their AI strategy. With both major technology players and start-ups launching independent efforts in the AI hardware space now, the window of opportunity for staking a claim will rapidly shrink over the next few years. Companies should be very clear on Where, How and When to play to capture the AI opportunity.

·       Hardware can be the differentiator that determines whether leading-edge applications reach the market and grab attention. As AI advances, hardware requirements will shift for compute, memory, storage, and networking—and that will translate into different demand patterns. The best semiconductor companies will understand these trends and pursue innovations that help take AI hardware to a new level. In addition to benefitting their bottom line, they’ll also be a driving force behind the AI applications transforming our world.

 Reference:

  •      Various internet blogs on AI
  •      Research papers by McKinsey
References:

external link

external link