Never miss a story from Verification(Verification of System and Software). Subscribe for in-depth analysis and articles.
Technological innovations have altered the way of living and disrupted the status quo. The world around us has become more connected, intelligent, and data-centric with zettabytes of data and artificial intelligence (AI). With the AI infusion in electronic design automation (EDA), the demands for more functionality, purpose-built silicon for performance enhancement, smaller form factors, etc., are continuously rising. Such trends initially started with mobile devices but now can be seen across cloud computing, automotive, high-performance computing, and many more applications. So, it is no longer viable to design components in isolation and necessitates the co-design and co-optimization of every element in an electronics system and every aspect of the nature of the system.
Further, different design fields such as electrical, mechanical, thermal, and computer are now intermingling, converging, and transforming into a new discipline. For instance, the speed and operation of an electric vehicle (EV) are interdependent on the heat and thermal condition of the electronic circuit and operating conditions.
We are moving beyond EDA to intelligent system design to address such challenges and issues. This article shares insights about Cadence's intelligent system design Strategy.
Integrating intelligent attributes (AI/ML) into emerging systems makes technology adaptive and evolving. These advanced machines are productive in meeting the demand with workflow automation and design optimization technologies. So, many companies have started including intelligence into their products and providing services and convenience not seen until now. These innovations are leading to new creative applications in day-to-day life. To keep up the pace, system companies are designing purpose-built semiconductors, and semiconductor firms provide software stacks that allow for significant hardware and software integration and optimization.
The intelligent system design approach incorporates. To keep up the pace, system companies are designing purpose-built semiconductors, and for optimization across software and hardware-software stacks are being delivered by semiconductor firms to differentiate their end products substantially.
These disruptions demand an integrated approach, where besides designing the chip, there should be interaction with the rest of the system running and optimizing. This vast computing resource makes it possible to replace limited personnel with unlimited computation, thereby increasing design team productivity. One such example is hyper-scale computing (HPC). As mentioned by TSMC, the HPC (high-performance computing) market has dethroned the smartphone market in Q1, 2022. The demands like higher reliability and longer resiliency necessitate the co-design and co-optimization of every component with the system's aspect.
To drive the technologies and products of the future, the world's most creative companies rely on end-to-end solutions across chips, IPs, packages, PCBs, and systems to meet stringent design requirements and deliver superior products. Cadence has evolved to meet these challenges, formulating its Intelligent System Design strategy to provide best-in-class computational software capabilities for all aspects of electronic system design. The Cadence Intelligent System Strategy enables the analysis of chip interactions amongst the system. Our intelligent computational software examines a system through multiple domains using pervasive intelligence. It improves technology innovation in hyperscale computing, 5G communications, aerospace and defense, automotive, and artificial intelligence (AI)/ machine learning (ML) related applications.
This is exciting and enables optimization across software and hardware to substantially differentiate the end products. For example, steering in F1 cars and monitoring the car performance together is no longer challenging, but it was in the past as it involved many sensors. Intelligent system design strategy has created a convergence of semiconductor design, system design, and system intelligence design and is beyond EDA. Another critical aspect of the system-based approach is that it helps move close to zero defects with improved yield, reduced time to market, and reduced cost.
We have formulated and delivered several transformative organic innovations in new system domains with our three-dimensional Intelligent System Design Strategy to meet the design requirement from the system perspective. Cadence's ISD strategy is based on three main pillars: Design Excellence, System Innovation, and Pervasive Intelligence. Adding AI/ML technologies under the hood of design tools, design flows, and IP for intelligence functionality in electronics systems enables intelligent system design. Today, with technologies—from EM analysis software to Optimality, CFD simulations, Cerebrus, and Tempus to Dynamic Duo—we have all that is needed for Intelligent System Design. OpenEye Scientific opens a new era of molecular modeling and simulation for us.
Cadence cloud computing setup accelerates workflow automation for a faster solution to complex problems. Our recent and upcoming system analysis innovations mark our shift to Intelligent system design. Shifting workloads to the cloud provides vast computing resources for computational software to use for design automation tasks.
Optimizing at the system level with the human-centric classical flow and legacy tools is challenging. The Intelligent System Design strategy enables a system design revolution and reduces project schedules with optimized continuous integration. Nevertheless, it is feasible to initiate complete system-level optimization, from the IC through the package and out via the board, in a fraction of the time, thanks to the AI/ML capability in the system tools and Cadence's Intelligent System Design philosophy. The Cadence tools can evaluate thousands of options by analyzing and learning from successful prior designs. The best result can be chosen by concentrating on the system-level requirements and utilizing machine learning to optimize decision-making.
An intelligent Systems Design strategy can offer a quicker route to an optimum design than conventional manual approaches. It can increase designers' productivity and analyzes efficiency by providing the ability to explore the entire design space.