Can AI catalyze the Semiconductor chip development ?

Abstract: Semiconductor industry is seeing new opportunity/investment for AI chip development and is racing against the time to catchup to meet the requirement. The complexity of the chip and time-to-market are huge challenges.  So naturally the question will arise what companies are doing to address this. While Semiconductor chips plays a huge role as business growth opportunity, the same technology can benefit the industry to address the challenges in complex AI chip development (and also benefit other domain chip design) and can the investment in this helps for better margin.  This blog will look at how AI can itself catalyse the semiconductor chip development.

Market opportunity –

Technology inflections such as 5G wireless, artificial intelligence, Internet of Things, cloud computing and machine learning are driving up the long-term demand for the chip industry. The artificial intelligence chip market was valued at $6,638 million in 2018, and is projected to reach $91,185 million by 2025, registering a CAGR of 45.2% from 2019 to 2025. Research shows that AI/ML now contributes between $5 billion and $8 billion annually to earnings before interest and taxes at semiconductor companies (Exhibit 2). This is impressive, but it reflects only about 10 percent of AI/ML’s full potential within the industry. Within the next two to three years, AI/ML could potentially generate between $35 billion and $40 billion in value annually. Over a longer time, frame—gains achieved four or more years in the future—this figure could rise to between $85 billion to $95 billion per year.

Challenges in front of Semiconductor chip industry –

Complexity – Cramming more and different kinds of processors and memories onto a die or into a package is causing the number of unknowns and the complexity of those designs to skyrocket. There are good reasons for combining all of these different devices into an SoC like capability of higher integration capacity in higher technology nodes, new methodologies and techniques of integrating more memories etc. If one see’s the complexity of AI chips it self the training chips ranges from 100sq mm to 400+ sq mm in die sizes in higher technology nodes (7nm and below). Due to higher integration the design and development complexity multiplies many folds. The following picture from cadence aptly explains this. It is clearly visible now to embrace new technologies and flows to aid development.

Time to Market – Time to market (TTM) is key for any product design. The products we are concerned having chips as integrated components, their development time steers the whole product development time. This itself (chip development) is getting challenged with increase in the complexity of the design taking more time. Sometime the present-day IT infrastructure and tools used create a bottleneck for development. So, it is going to be apparent to come up with new methodologies and adapt new technologies to speed-up, and cut development time.

Cost of development – Cost of development increasing and hence puts pressure of the profit margin. Mere increase in complexity forces more skilled man power, new tools, and increased compute resources. Increasing development resources will not help as we move with increased complexity, instead may create more hurdles to manage. So the point we are reaching now to see how we are going to re-look at our development to take advantage of new technologies in the development cycle. The new technology in development will itself is evolving and requires investment. So, companies have to have long term strategy to mange this with clear focus along with intended outcome.

 

Does AI itself comes as a catalyst for semiconductor chip development?

Surely Yes. AI itself will play a major role in chip development. This is being slowly getting proved in the industry. Let us look at the market survey result conducted by McKinsey in this area.

A comprehensive case-study shows that AI & ML will help in each development phase of Semiconductor chip and most of the benefits can be seen in manufacturing and Chip-design phase. The survey compares the current gains vs Near and long-term gains if Ai-ML solutions are deployed in development.

Let us look at just one development phase of Chip design and see how and where AI & ML can play a role. IF the development phased deployed with AI & ML enabled flows, companies can avoid time consuming iterations, accelerate yield ramp-up, and decrease the costs required to maintain yield. They may also automate the time-consuming processes related to physical-layout design and the verification process.

Although we are not yet at the point where AI/ML acceleration can be applied to all designs and to all stages of chip design, we do not see a fundamental reason why it cannot penetrate further over time.  Above all, scaling AI/ML efforts must be a strategic priority for companies. The initial effort, which involves coordinating data, agreeing on priority use cases, and encouraging collaboration among the right business, data-science, and engineering talent, is too great to be successful as a bottom-up project, so what it means is a meaningful R&D strategy to be in-place with clear road-map. One way to do it is Ideally, the AI/ML effort will be linked to clear business targets, giving business units and business functions a joint interest in making the transformation successful.

For example, companies could identify cost savings development phases that are suits most and get to achieve the goal and result. The major time-consuming development phases like functional verification and physical design could be the use-cases. If current acquired human knowledge is utilised to come-up with appropriate Ai models and then tune them with on-going experiments, it is possible to see the end results.

This is even true for the VLSI design CAD tool vendors to see how they can involve AI & ML technology to upgrade their offering and helping the design & development community. If a collaboration can happen with tool vendors and chip design companies, there are umpteen possibilities of various development flows and methodologies can evolve which can help the chip development in turn the bottom line each company is looking. For a CAD tool company, they can offer AI & ML feature enabled tools to gain more margin for their tool cost.

This is another huge opportunity for universities and research institutes to work and collaborate with companies to help and develop the AI/ML technology. As AI/ML’s potential is still to explore and more innovations needed for better deployment of this technology across wide variety of market segment. Currently AI/ML is getting deployed widely into e-commerce, IoT, Automobile and other sectors like Health-care, energy, education, defense/military are also getting more traction. Every industry requires fine tuning the data model and training pattern and may require customization. This opens-up opportunity for innovation and collaboration for University/Research institutes along with industry/Govt sectors

Companies must allocate sufficient resources to their AI/ML initiatives and investigate supportive partnerships with third parties that have complementary skills, rather than trying to reinvent the wheel themselves. Some larger players may have the spending power required to develop most capabilities in-house, as well as sufficient data from their large installed tool fleet to train AI/ML models, allowing them to retain full control over all associated intellectual property. Given the required resources, smaller players might find it beneficial to leverage commercially available solutions where available, or to partner with others to develop or share algorithms, or to create joint data-sharing platforms that increase the amount of information available to train models. Examples of potential partners include other semiconductor device makers, companies involved in electronic design automation, hyperscale cloud providers, or equipment OEMs.

 

Conclusion –

The semiconductor industry is at a turning point, and companies that don’t devote significant resources to AI/ML strategies could be left behind. Although semiconductor companies may take different approaches, depending on business model, experience with AI/ML, and strategic priorities, the goal is the same: to take productivity and innovation to new levels. If they set themselves to do it in a planned way and achieve the goal, then AI/ML may eventually reduce the current R&D cost base and similar approaches can be taken to other development steps to leverage the cost base from AI/ML.

 

References

·       Research data from McKinsey & Company

·       AI/ML related blogs applicable to Semiconductor