2026 Keynote Speakers

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. I will describe my thesis work on macroscopic quantum tunneling and energy-level quantization that led to the Nobel Prize, as well as several other important experiments that advanced superconducting qubits.  Nevertheless, the path toward a full-stack scalable technology is a work in progress. There are significant outstanding quantum hardware, fabrication, architecture, and algorithmic challenges that are either unresolved or overlooked. Here, we show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits and employing system engineering approaches.

AI is emerging as the foundational capability that defines the user experience and is reshaping the semiconductor industry into a high volume, multi-market business spanning an unprecedented diversity of power envelopes and form factors—from sub-5W battery-powered devices like AI pins and up to 500W cloud servers. This transition is also driving distributed, hybrid AI across personal devices, edge, and cloud, enabling personal AI systems that deliver low latency, improved privacy, and higher reliability – essential benefits of Edge AI.

To design silicon products that address the diverse AI workloads, power budgets, and form factors for these markets, we can leverage a common, modular IP library —including CPU, GPU, AI accelerators, connectivity, and security. However, the current process of adapting each IP and integrating it into a new SoC demands significant human effort. To minimize the NRE, we need tools that can support and automate the process, enabling engineering teams to quickly pivot and assemble SoCs that can meet the full spectrum of needs, from very low-power requirements to very high-performance.

Moreover, we face additional challenges with the slowing of Moore's law and the rise of chiplets. This necessitates new tools for 3DIC floor planning, package design, and thermal design.

During the talk, I will focus on the gaps between the current state of EDA tools and the needs of the semiconductor industry to leverage common IP across diverse products. I will also share perspectives on approaches that help to close these gaps.

In a keynote address at the 2007 Design Automation Conference, I discussed the potential of an emerging technology known as synthetic biology and the role integrated circuit design methodology could play in shaping this nascent field.

Now almost 20 years later, synthetic biology has matured significantly. Today It offers the opportunity not only of addressing genetic diseases, but also of engineering life itself. In fact, we are witnessing the simultaneous emergence and explosive growth of three technologies that are bound the shape of the future of humanity: genetic engineering (such as enabled by CRISPR-CAS9), artificial intelligence, and brain-machine interfaces (BMIs).

While all three are equally influential, this presentation will focus primarily on the latter – that is, direct interfaces with the human brain. Brain-machine interfaces are systems that create a direct communication pathway between the brain and external devices. By translating neural signals into actionable information, BMIs can be used to restore lost abilities, augment human capabilities, and enable entirely new forms of interaction with technology. They hold the promise of revolutionizing fields such as medicine, rehabilitation, neuroscience, and even human-machine collaboration.

We will review the state-of-the-art, identify progress, speculate where it may lead, and address some pertinent questions such as privacy and morality implications. But most importantly, we will explore the essential role that the design community can —and must—play in the conception, design, integration and deployment of these groundbreaking technologies.