Financial Data and Key Metrics Changes - Cadence reported total revenue of $1.339 billion for Q3 2025, with a GAAP operating margin of 31.8% and a non-GAAP operating margin of 47.6% [14] - GAAP EPS was $1.05, while non-GAAP EPS was $1.93 [14] - The company raised its full-year outlook to approximately 14% revenue growth and 18% EPS growth [4][12] Business Line Data and Key Metrics Changes - The IP business is tracking over 20% growth for the second consecutive year, driven by demand in AI, HPC, and chiplet architectures [20][21] - The core EDA business saw strong results, with significant adoption of AI-driven design and verification solutions [9] - Hardware had a record Q3, with notable expansions at AI and HPC customers [10] Market Data and Key Metrics Changes - Bookings exceeded expectations, resulting in a backlog of over $7 billion [4][13] - China experienced a year-over-year growth of approximately 53%, indicating a return to normal business activity post-regulatory changes [35][36] Company Strategy and Development Direction - Cadence is focusing on the AI megatrend, positioning itself to capture opportunities across various industries with a comprehensive portfolio [5][12] - The company is expanding partnerships with major semiconductor companies, including TSMC and Samsung, to support next-generation AI flows [6][10] - Recent acquisitions, such as Hexagon's T&E business, are aimed at enhancing capabilities in structural analysis and multi-body dynamics [11][44] Management's Comments on Operating Environment and Future Outlook - Management expressed optimism about the ongoing strength of the business and the AI infrastructure build-out, which is expected to drive future growth [25][26] - The company anticipates continued strong demand across all business lines and geographies, with a disciplined approach to growth [30][32] Other Important Information - The company plans to use at least 50% of its annual free cash flow for share repurchases [16] - Operating cash flow for Q3 was reported at $311 million, with a cash balance of $2.753 billion at quarter-end [15] Q&A Session Summary Question: What is driving the growth in the IP business? - Management highlighted that the IP business is focused on AI and HPC at advanced nodes, with strong customer demand and partnerships with major foundries [20][21][23] Question: Are there still renewal opportunities in Q4? - Management confirmed that strong demand continues, particularly in AI infrastructure, and that renewals are expected to contribute positively [24][25][27] Question: What is the outlook for hardware demand? - Management indicated that hardware demand remains strong, with expectations for continued growth into 2026 [56][58] Question: How is the company positioned in the system design market? - Management emphasized the strategic acquisitions aimed at enhancing capabilities in simulation and analysis, positioning the company well for future growth [40][44] Question: What are the expectations for China’s growth? - Management noted that design activity in China is back to normal, with expectations for continued growth, contingent on geopolitical stability [73][74] Question: What are the OpEx dynamics for Q3 and Q4? - Management explained that Q3 performance was better than expected due to a small restructure, while Q4 may see some new expenses from acquisitions [76][78]

Core Insights - Sinopec Petroleum Engineering Design Company has developed an intelligent auxiliary evaluation system for pipeline radiographic defect detection, achieving a defect identification accuracy of 96% during testing, comparable to experienced human experts [1] - This achievement signifies a significant step towards the intelligent and automated transformation of non-destructive testing in long-distance pipeline engineering, promoting the application of AI technology in engineering design [1] Technology and Innovation - The intelligent system features its own knowledge base, a radiographic defect database, and employs advanced algorithms such as adaptive threshold histogram equalization and guided filtering to enhance image clarity [1] - By integrating the YOLOv7 model with an improved feature fusion network, the system accurately identifies and locates defects such as cracks, pores, and lack of fusion in pipelines, demonstrating the practical application of AI in non-destructive testing [1] Efficiency and Application - The system has improved the efficiency of evaluation by over 10 times, transforming the traditional reliance on human labor and experience, significantly enhancing detection accuracy and reducing subjective error risks [2] - The development team is continuously optimizing the system's functionality to enhance its applicability in various complex welding scenarios and is accelerating the pilot application of the system in pipeline engineering projects [2]