Cadence Expands AI Collaborations with Nvidia and Google Cloud
At its recent CadenceLIVE event, Cadence Design Systems unveiled two strategic collaborations centered on artificial intelligence, extending its partnership with Nvidia and launching new integrations with Google Cloud. These alliances aim to advance AI applications in robotics, semiconductor design, and cloud computing.
Combining AI with Physics-Based Simulation for Robotics
The renewed partnership with Nvidia focuses on integrating AI with physics-based simulation and accelerated computing to improve robotic systems and system-level design. This approach targets modeling and deployment in semiconductors and large-scale AI infrastructure, encompassing robotic systems Nvidia terms “physical AI.” Cadence is merging its multi-physics simulation and system design platforms with Nvidia’s CUDA-X libraries, AI models, and the Omniverse simulation environment.
This integration enables engineers to accurately simulate thermal and mechanical interactions, assessing system behavior under realistic conditions. Beyond chip design, the tools also address infrastructure components such as networking and power systems. The combined platform facilitates comprehensive system performance analysis prior to physical deployment, emphasizing the interdependent operation of computing, networking, and power elements.
Robotics Development Powered by Simulation
Cadence’s physics engines, which model real-world material interactions, are linked with Nvidia’s AI models for training robotic systems within simulated settings. This approach reduces the necessity for extensive real-world data collection by generating datasets through physics-based models rather than physical data acquisition. According to Cadence CEO Anirudh Devgan, “The more accurate the generated training data is, the better the model will be.” Nvidia CEO Jensen Huang highlighted the collaboration by stating, “We’re working with you in the board on robotic systems.” Industrial robotics firms such as ABB Robotics, FANUC, YASKAWA, and KUKA utilize Nvidia’s Isaac simulation frameworks and Omniverse digital twin tools for virtual commissioning, enabling software testing of production systems before physical deployment.
New AI Agent for Cloud-Based Chip Design Automation
Separately, Cadence introduced a new AI agent designed to automate late-stage chip design tasks, specifically focusing on physical layout processes that translate circuit designs into silicon implementations. This complements an earlier AI agent for front-end chip design, which handles circuit definition. The new agent will be accessible via Google Cloud, combining Cadence’s electronic design automation tools with Google’s Gemini models to automate design and verification workflows. This cloud-based solution allows teams to perform complex workloads without on-premises infrastructure.
Cadence’s ChipStack AI Super Agent platform employs model-based reasoning integrated with native design tools to coordinate multiple design stages autonomously, interpreting requirements and executing tasks across the design pipeline. Early deployments have demonstrated productivity gains of up to 10 times in design and verification tasks, although specific customer details remain undisclosed. Devgan emphasized, “We help build AI systems, and then those AI systems can help improve the design process.” The collaborative platform also utilizes digital twin models to validate systems virtually, enabling engineers to evaluate trade-offs, performance scenarios, and optimize configurations before physical production. Both companies highlighted how the complexity and costs of large-scale data center infrastructure restrict trial-and-error deployment strategies.
Nvidia’s Announcement of Open-Source Quantum AI Models
In a separate development, Nvidia introduced NVIDIA Ising, a suite of open-source quantum AI models based on the Ising mathematical framework representing physical system interactions. These models aim to enhance quantum processor calibration and error correction, delivering up to 2.5 times faster performance and triple the accuracy in decoding for error correction processes. Huang stated, “AI is essential to making quantum computing practical. With Ising, AI becomes the control plane – the operating system of quantum machines – transforming fragile qubits to scalable and reliable quantum-GPU systems.”
This expanded collaboration highlights the growing role of AI in advancing robotics, semiconductor design, and quantum computing, reflecting broader industry trends toward integrating AI deeply within physical and digital systems.
Fonte: ver artigo original
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