For observers of EDA markets there is an easily overlooked opportunity for new growth. Today around 50% of EDA revenues come from systems rather than semiconductor companies, from datacenters to automotive, aerospace, energy, and others. In most of these industries total system design depends as much on mechanical and other multiphysics optimizations (aerodynamics, stress, thermal, electromagnetics, etc.) as on electronic design. Multiphysics analysis has already penetrated semiconductor design, for example in-package to in-system thermal analysis and management using computational fluid dynamics (CFD) for cooling analytics. In short, multiphysics bridges between electronic system design and total system design as critical to support power generation, airline, and automotive markets. As in chip design, system problems in these domains keep getting harder, demanding active and continuous innovation from solution providers to address modern design needs. Exploiting synergies between EDA and multiphysics expertise, Cadence claims the Millennium platform delivers a breathtaking performance advance for multiphysics analytics, solving industrial scale problems in hours rather than weeks and opening large new growth opportunities.
A Fast Layman’s Guide to CFD
CFD simulates the flow of a fluid (liquid or gas) around/through mechanical structures like circuit boards, aircraft, gas turbines and cars. Without CFD, these measurements must be made on prototypes, for example in wind tunnels, an expensive and time-consuming process. With CFD, engineers can shift-left (a familiar concept in EDA), to study performance of a digital twin against simulated fluid flows.
Simulations are based on the Navier-Stokes differential equation, mapped across discrete meshes to enable numerical solving. Meshes are designed for finer spacing around critical zones with coarser spacing elsewhere and commonly run to many millions of elements. Factors considered in solving across the mesh include pressure and temperature, also viscosity because all fluids flow slower near boundaries. Compressibility can be important when considering acoustics or Mach speeds; turbulence is another factor at high speeds. These factors have enough impact on mesh and solver methods that CFD must provide a family of technology solutions.
Turbulence is the most challenging condition to simulate accurately. The most widely practiced technique in industry today develops static averages, a weak approximation for a dynamic phenomenon able to deliver accurate CFD around an airplane wing at cruising altitude but not during ascent or descent. A different technique called Large Eddy Simulation (LES) can model much more accurately and dynamically but is more expensive in computation, making extensive turbulence modeling through a digital twin impractical. Thus critical analyses have been limited to real physical modeling using prototypes in wind tunnels, effective but too cumbersome to explore thousands of scenarios for optimization.
Cadence Authority in CFD and LES
CFD is a high expertise domain with a lot of history. Tool departments and often product teams are staffed with armies of PhDs. Algorithms for meshes and solvers, together with software, have evolved significantly and of course continue to evolve. In other words this is a domain an EDA company must enter inorganically.
Cadence started here in 2021 with a series of acquisitions. These include NUMECA with strong meshing and solver technologies and an established reputation in marine and turbomachinery applications. Shortly after Cadence acquired Pointwise with proven strength in CFD meshing and established in aerospace and defense markets. In late 2022 they acquired Cascade Technologies, a Stanford spinout with compelling technology for LES. Through these acquisitions Cadence has built a stable of thoroughbred technology and experts in CFD, adding to their established strength in other aspects of multiphysics. But it seems they didn’t stop there.
Industries are desperate for higher LES performance for more accurate digital twin modeling. As one example, 50% of the energy consumed by a car goes to overcoming aerodynamic drag, directly affecting ICE fuel consumption or EV range. Designers need digital twins to simulate over thousands of operating conditions to find and optimize the many small improvements they can make around the car structure to reduce drag. How did Cadence step up to this need?
Cadence Millennium M1 and the Fidelity LES Solver
CFD is very parallelizable, so an obvious solution is run a job across many server/CPU clusters. This was already possible on big CPU farms or supercomputers, but cost becomes prohibitive when running complex LES algorithms over very large meshes with experiments over thousand of runs. Overcoming this barrier has been one of the drivers prompting development of Millennium M1, Cadence’s first GPU-based accelerator.
Cadence has a proven track record in hardware acceleration across multiple generations of the Palladium and Protium platforms for hardware verification. They have worked out the design, operations, and supply chain kinks to build these platforms and they have established infrastructure to provide cloud-based access. (All platforms including Millennium can also be purchased for on-premises analysis.) Extending this expertise to a GPU-based platform is both obvious and brilliant. In one stroke (though I’m sure it took them time to get there 😀) they can accelerate CFD simulations. Adding new generative AI methods for design and analysis exploration they claim delivers up to 100X design impact in accuracy, speed, and scale at much lower power when compared with massive CPU server parallelism. Hardware acceleration from Cadence hardware know-how combined with genAI expertise from both EDA and CFD teams demonstrates the synergy required to deliver the active and continuous innovation I mentioned earlier.
CFD algorithm development has also been very active. Software is designed from the ground up to be GPU native. Problem preparation for analysis includes low-touch optimized mesh generation. And there are new numerical methods to ensure high stability in LES simulations (normally prone to unphysical behavior in turbulence modeling).
This capability is available today for CFD multiphysics modeling, in the cloud or on-premises.
Millennium is Not Just for CFD
It is obvious that a GPU-based accelerator should be able to do more than accelerate CFD. It could accelerate finite element analyses such as stress, thermal diffusion, and electromagnetics. It can also run generative AI. But why not just use one of the giant hyperscaler GPU banks for that purpose? For me one reason is simply availability and latency in competition with chatbots and creative image apps. Equally it is hard to believe that application-specific fine-tuning on top of a mass market LLM models could serve the high complexity, high accuracy, and domain-specific needs of modern EDA and multiphysics software. Dedicated hardware is the way to go, accessible through the cloud or in on-premises installations.
It will be very interesting to see what capabilities Millennium will offer in the future both for electronic design and for multiphysics. You can learn more HERE.