By Bob O’Donnell, president and chief analyst of TECHnalysis Research While much of the technology world is still searching to find easily measurable productivity improvements with generative AI, it turns out there’s another tech trend that’s already driving very impressive results today: digital twins. The idea behind digital twins is to create a complete replica of a physical system in software and then use that digital copy to run simulations and other tests before building a physical implementation of the device or system in the “real world.” Software-Based Systems As you can easily imagine, the ability to create, refine, and test complex configurations of various elements in software before committing to a hardware design offers incredible benefits to a system’s designers. In addition to ensuring that a given design works as intended, a digital twin offers the opportunity to refine the entire system or any combination of individual elements. Organizations can focus on optimizing a design for the best possible performance, most efficient energy usage, or any number of other targets that a company may have for a specific product. Digital twins can also be used for testing under conditions that aren’t practical in the real world, such as crashing a car or jet engine thousands of times in thousands of different situations. In addition, one of the more intriguing new applications of GenAI is to use it to help refine this system optimization process. Appropriately trained foundation models can run through billions of permutations and help ensure that a digital twin of a given system is as good as it can be by “solving” for the best potential outcome. They can also create design performance levels that existing methodologies don’t even begin to meet. The real trick in getting this all to work is finding the right software solution to build the digital twin. Not surprisingly, the type of device being “modeled” in digital form impacts this decision, but one of the most important overarching demands is the ability for a tool to faithfully replicate in software how individual elements behave in the real world, both on their own and in combination with other elements. It turns out that tools originally designed to digitally create some of the most complex and sophisticated devices that humankind has ever conceived—the semiconductor chips powering today’s digital devices—are extremely well suited to this digital system emulation task. Why Cadence? That’s why a company like Cadence, which was founded to create and is best known for its EDA (electronic design automation) chip design software, has become an important force in the world of digital twins. In solving the technical challenges associated with the chip design process, Cadence created software capabilities that can be applied to building an enormous range of sophisticated systems, including jet and automobile engines, manufacturing equipment, computing systems, and much more. Much of the work is focused on what the company refers to as computational software—that is, software that can solve the immensely complex physical and computational problems that underly and mathematically “describe” how the different elements in a system function on their own and in combination with other elements. It is these capabilities that allow Cadence software tools such as Allegro X Design Platform and Clarity 3D Solver to accurately emulate in software the real-world functionality and performance of systems that integrate various components. In the case of systems like jet engines that need to function well in volatile real-world environments, companies can use Cadence’s Fidelity CFD (Computational Fluid Dynamics) Platform . This allows designers to see how their designs operate in different types of weather, at different altitudes, in dangerous conditions that can’t be replicated in the real world, and much more—again, all before committing to an expensive hardware prototype. Of course, the EDA tools that Cadence is best known for—like Virtuoso Studio and Xcelium Logic Simulator —allow these capabilities to be brought to individual semiconductor chip designs. Before they go into production, all the world’s biggest chip companies use Cadence tools to create, refine, and digitally test their latest generation chips. With the move to chiplet architectures that bundle together multiple individual chips into larger, more sophisticated SoCs (system on chips) in 3D packages, Cadence has a tool called Integrity 3D-IC Platform for creating chiplet designs. Once it comes time to create customized chips for some of these applications, Cadence also offers hardware platforms such as Palladium Emulation and Protium Enterprise Prototyping that organizations can use to prototype and validate their designs before they commit to manufacturing. On top of that, many companies then use other Cadence tools like the aforementioned Allegro X Design Platform to link together multiple independent chips and other elements into a computing system design. Building even further, Cadence has recently released a set of tools called Cadence Reality Digital Twin Platform for modeling the performance, power consumption levels, and operation of data centers that integrate thousands of different computer systems. Given the enormous power demands that today’s GPU-rich cloud and enterprise data centers have, the ability to optimize the overall data center design for more power efficiency is an absolutely essential need. Biological Systems What’s fascinating is that these system-level design capabilities extend beyond the world of hardware into fields like biology and drug discovery as well. In fact, Cadence’s 2022 acquisition of OpenEye and the new product efforts associated with it are centered around modeling how complex biological systems will react to newly created drugs. In this case, Cadence tools such as Orion Molecular Design Platform essentially create digital twins of various types of human cells and then model how various molecules from a new type of drug will interact with those cells. Using these types of technologies, companies can rapidly expand the range of different molecules that they test in the digital domain without going through the very difficult and time-consuming task of creating and testing physical samples of these new molecules. While the work is still in its early phases, the potential of combining this technology with appropriately trained GenAI models could lead to significantly faster drug discovery efforts. Given the computational power now available to us—thanks in no small part to the impact that EDA tools like the ones from Cadence have had over the last several decades—the ability to emulate complex systems completely in software through the process of digital twinning has never been more compelling. From designing individual ICs (integrated circuits), through compute systems, and even into areas like computational fluid dynamics and drug discovery, the possibilities that digital twins—and the tools that create them—have enabled offer the potential to dramatically impact industries of all types and sizes. It’s going to be exciting to see how their impact continues to grow. Bob O’Donnell is the president and chief analyst of TECHnalysis Research, LLC , a market research firm that provides strategic consulting and market research services to the technology industry and professional financial community. You can follow him on Twitter @bobodtech .

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