Compare Ansys Totem vs. NVIDIA Modulus

Ansys Totem-SC stands out as the established and reliable leader in the realm of power noise and reliability validation for both analog and mixed-signal designs, utilizing a cloud-native elastic compute framework. Recognized as the benchmark solution for voltage drop and electromigration multiphysics sign-off at the transistor level, Ansys Totem-SC has proven its effectiveness across numerous tapeouts, leveraging a cloud-based infrastructure to provide the necessary speed and capacity for comprehensive full-chip assessments. Its accuracy in signoff has been validated by all major foundries, supporting advanced finFET technologies down to 3nm. This platform excels in power noise and reliability analysis specifically for analog mixed-signal intellectual property and fully custom designs. Moreover, it generates IP models that facilitate SOC-level power integrity signoff in conjunction with RedHawk-SC and develops compact chip models for power delivery networks applicable at both chip and system levels. The solution is not only industry-proven but also certified by foundries, making it a highly regarded choice for analog and mixed-signal electromigration and IR analysis. With Ansys Totem-SC, designers can confidently ensure the integrity and reliability of their power delivery systems throughout the design process.

NVIDIA Modulus is an advanced neural network framework that integrates the principles of physics, represented through governing partial differential equations (PDEs), with data to create accurate, parameterized surrogate models that operate with near-instantaneous latency. This framework is ideal for those venturing into AI-enhanced physics challenges or for those crafting digital twin models to navigate intricate non-linear, multi-physics systems, offering robust support throughout the process. It provides essential components for constructing physics-based machine learning surrogate models that effectively merge physics principles with data insights. Its versatility ensures applicability across various fields, including engineering simulations and life sciences, while accommodating both forward simulations and inverse/data assimilation tasks. Furthermore, NVIDIA Modulus enables parameterized representations of systems that can tackle multiple scenarios in real time, allowing users to train offline once and subsequently perform real-time inference repeatedly. As such, it empowers researchers and engineers to explore innovative solutions across a spectrum of complex problems with unprecedented efficiency.