From GPUs to Analog Chips: What Patent Trends Reveal About the Future of AI x Semiconductor Innovation

Toni Njim, Chief Product Officer at Anaqua

 

Over the past five years, AI-related semiconductor patent filings grew by more than 114%. This highlights not only the growth of the semiconductor industry itself, but more importantly how AI has evolved into a primary driver of R&D investment in semiconductors.

 

AI has become one of the most defining technologies of the decade, and while its adoption continues to accelerate, so does the pressure on the hardware infrastructure behind it. In fact, every computation and every query answered runs on silicon that is specifically built to support this technology.

 

Anaqua's latest report analysed more than 700,000 global semiconductor patent filing data to examine how innovation is unfolding across the sector and provide insight into where organizations are investing today and where future development may emerge.

 

A window into the future of AI infrastructure development

Controlling AI infrastructure increasingly requires control over the underlying hardware technologies that power it.

 

In light of this, the semiconductor industry is currently experiencing a significant expansion in the range of organizations contributing to innovation. Traditional chipmakers remain influential, but they are no longer the only participants shaping the future of AI hardware.

 

Patent filings reveal growing activity from hyperscale cloud providers, government-backed research institutions, universities and a new generation of specialist AI chip developers. Much of this growth can be traced back to the semiconductor shortages of 2021 and 2022, which prompted investment in domestic chip development to reduce overreliance on external supplier networks.

 

Google and Microsoft are a good example of how hyperscalers have also emerged as silicon designers, in their own right. They have established significant patent positions in AI chip development, resulting in a new phase of vertical integration where ownership of AI infrastructure extends beyond data centres and software platforms into semiconductor design itself.

 

China has also developed as a major contributor to semiconductor patenting – spanning universities, research institutes and governing entities, particularly when it comes to inference computing. For the country, patent filings suggest that AI hardware has become an increasingly strategic priority at both corporate and national levels.

 

From GPUs to Inference Chips

The first wave of the AI boom was largely defined by training large language models on Graphics Processing Units (GPUs). GPUs became the default architecture of choice for training large-scale neural networks because of their ability to process vast numbers of calculations simultaneously. GPU-titled patents grew by over 108% over five years. Intel, AMD, China's governing bodies and Qualcomm were major players in this context.

 

An outlier worth pausing on is NVIDIA, which filed a lower number of patents despite dominating the GPU market. The company built its competitive moat through its CUDA software ecosystem.

 

In the past few years, however, the AI industry has shifted from training to inference, where AI systems apply what they have learned to produce outputs from new data. While GPUs can still perform inference calculations, they do so at a significant cost in terms of power consumption and heat generation. This called for a versatile solution that ultimately yielded AI accelerator chips, or inference chips, designed specifically for LLMs neural networks, and built to match GPUs performance at a fraction of the cost.

 

Patent activity in this area accelerated accordingly, with significant filings from Samsung, Google, Intel, Qualcomm and Chinese research entities.

 

A common battleground: Chip Architecture

Underneath the chip-level competition is a quieter fight over architecture. At the IC (integrated circuit) architecture-level, innovation increasingly centers on processor design, memory hierarchies, data movement, parallel computing structures and power management. These areas set the ceiling on the scalability of AI workloads. It's also where a lot of AI-specific innovation is now concentrated.

 

AI architecture patent filings reached more than 15,000 over the five-year period, with Samsung leading at 1,194 patent filings, followed by Intel and Google. Qualcomm and Huawei recorded some of the fastest growth in architectural patenting over the period.

 

This trend reflects a changing understanding of AI performance. For many organisations, future competitive advantage may depend less on increasing processing power and more on reducing the energy and cost required to move and process data.

 

The next frontier: Analog

Modern AI systems require enormous amounts of computational power, and much of the energy consumed by digital architectures is spent on high-frequency switching between memory and processors. As AI models grow in scale, the ceiling of performance created by binary chips becomes increasingly difficult to ignore and can put constraints on future AI developments.

 

Patent filings in the semiconductor industry suggest companies are exploring architectures designed specifically to optimise computation. With analog AI systems, computing is performed directly where data is stored, mimicking the efficiency of biological neural networks, which dramatically reduces the energy costs associated with moving information throughout a chip.

 

Among the more than 5,700 patents identified in this area, Samsung, IBM and Microsoft rank among the leading filers. What's particularly interesting is the variety of approaches being explored.

 

From phase-change memory technologies with IBM, to neuromorphic computing employed by Intel, the sector is attempting to rethink how AI workloads are processed rather than simply improving existing architectures.

 

Microsoft is taking perhaps the boldest approach by exploring optical computing. By using light rather than electricity to perform calculations, Microsoft could eventually deliver efficiency gains beyond those achievable through today's leading GPU architectures.

 

The competitive landscape has shifted from company versus company to AI architectures, and what the major players are developing could shape how future computing systems operate.

 

Different bets for the future of computing

For decades, semiconductor competition was largely defined by making chips smaller and more powerful. The AI era is reshaping those priorities.

 

From inference accelerators and AI-specific architectures to neuromorphic processors and analog computing, semiconductor filings suggest organizations are exploring multiple paths forward rather than converging around a single solution.

 

The patent landscape offers an early view into how the industry is thinking about the challenge ahead. Increasingly, the focus is not simply on creating more computational power, but on finding more efficient ways to deliver it.