With the evolution of modern technologies, the demand for complex integrated circuits is growing rapidly. Integrated circuits are complete systems that also include software. These complex integrated circuits are designed to increase reliability, decrease cost per component, increase component density, and improve the speed of electronic devices. Integrated circuits are used in various electronic devices such as wristwatches, radar, computers, logic devices, and video processors. There is a growing demand for complex integrated circuits across various industries. The process of designing integrated circuits is much more complex without the use of electronic design automation. Electronic design automation simplifies the process of designing complex integrated circuits, saving time and money.
Several companies are offering electronic design automation solutions integrated with cutting-edge technologies such as Artificial Intelligence & Machine Learning to reduce the workload of design engineers. For instance, in July 2021, Cadence Design Systems, Inc. (U.S.) launched the Cerebrus Intelligent Chip Explorer machine learning tool to automate the chip design process and increase the productivity of design engineers. Also, in December 2021, Nexar (a part of Altium Limited) (U.S.) partnered with SnapEDA (U.S.) to improve the performance of printed circuit board (PCB) designs and help engineers design electronic circuits fasters by providing computer-aided design (CAD) models and software. Thus, the need to reduce the complexity of integrated circuit designing boosts the demand for EDA solutions, driving the growth of this market.
Increasing Integration of Machine Learning Technology is Expected to Offer Significant Growth Opportunities for the Electronic Design Automation Market
Electronic design automation providers work to build advanced tools, platforms, and software products to help reduce the costs associated with the testing, simulation, and verification of semiconductor chips before actual manufacturing. The latest technologies supporting EDA are cloud computing, the Internet of Things (IoT), Artificial Intelligence (AI) & Machine Learning (ML), 3d printing, and Robotic Process Automation (RPA), among others. Electronic design automation is gaining traction due to increasing investments and the growing need for EDS solutions powered by machine learning for high performance. The incorporation of technologies such as machine learning has enhanced and simplified computer-aided design, VLSI design, and their intersections.
Machine learning supports EDA tools by analyzing past experiences and using the information as training data to expand the modeling and simulation capabilities of EDA tools. This provides insights into designs and improves the accuracy and efficiency of design tools. It also helps simulate the design to predict the outcome. Machine learning also helps automate the chip designing process. These benefits boost the demand for EDA solutions powered by machine learning technology, creating opportunities for the players operating in this market.
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