Cadence has introduced the Palladium Dynamic Power Analysis (DPA) App, enabling semiconductor and systems designers to accurately measure and optimize power usage in billion-gate AI and ML chip designs before silicon production. Developed in close collaboration with NVIDIA, the solution leverages the Cadence Palladium Z3 Enterprise Emulation Platform to analyze billions of cycles in just two to three hours, achieving up to 97% accuracy under real-world workload conditions.
Traditional power analysis tools have struggled to scale beyond a few hundred thousand cycles without incurring prohibitive runtimes, limiting their usefulness for complex AI and GPU-accelerated systems. The new hardware-assisted approach from Cadence and NVIDIA uses parallel processing and emulation acceleration to deliver a breakthrough in early power profiling. This allows engineers to meet aggressive performance and efficiency targets while avoiding costly post-silicon redesigns.
The Palladium DPA App integrates with Cadence’s broader analysis and implementation suite, enabling power estimation, optimization, and signoff across the design lifecycle. It is particularly suited for AI, ML, and GPU-intensive workloads, where early-stage power modeling can significantly reduce over- or under-design risks.
• Processes billions of design cycles in 2–3 hours with up to 97% accuracy
• Enables real-time, hardware-accelerated power profiling for billion-gate designs
• Supports AI, ML, and GPU-accelerated applications for greater energy efficiency
• Fully integrated into Cadence’s Intelligent System Design™ workflow for continuous optimization
• Jointly developed with NVIDIA to align with next-gen accelerated computing demands
“Cadence and NVIDIA are building on our long history of introducing transformative technologies developed through deep collaboration,” said Dhiraj Goswami, corporate vice president and general manager at Cadence. “This project redefined boundaries, processing billions of cycles in as few as two to three hours. This empowers customers to confidently meet aggressive performance and power targets and accelerate their time to silicon.”
🌐 Why it Matters
With AI/ML chip designs pushing past the billion-gate mark, power efficiency has become as critical as raw performance. This joint Cadence–NVIDIA effort moves power analysis from a late-stage, slow process to an early, rapid, and scalable capability—allowing design teams to fine-tune energy efficiency before tapeout. The shift will be particularly impactful for data center AI workloads, where even small efficiency gains translate into significant operational and cooling cost savings.
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