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AI technology complements the brain power and efforts of humans in many ways: it gives us insights into various health parameters, helps us produce things more efficiently, and brings a deeper understanding of what¡¯s happening in our environment, for example. Now, AI is even helping engineers design chips for AI applications.

It¡¯s not that AI is taking over for engineers. Rather, the AI-driven chip design technologies complement the efforts of engineers, taking on the more repetitive tasks so that engineers can focus on more value-added work. And, given that engineering resources are so constrained these days, AI enables design teams to do more with less.

Perhaps you¡¯ve heard about new innovations enabling AI in chip design. AI-driven design space optimization analyzes large data streams generated by design tools. Learning in real time and providing quick, yet meaningful, results, this technology can help you achieve power, performance, and area (PPA) targets faster. As such, it lets you devote more time to product differentiation, higher yield design spaces, and other critical efforts.

Maybe your team is starting to explore some of these tools, such as ²ÝÁñÉçÇø DSO.ai?, the industry¡¯s first autonomous AI application for chip design. But you¡¯ve still got some lingering questions:

• Will applying AI solutions help?
• How do I get started with AI in chip design?
• When should I start using AI and does it scale?
• What tasks should I use AI for?

To help you navigate the increasingly intelligent world of chip design, we¡¯ve started a new AI podcast. Each segment is a few minutes long and answers key questions, with insights provided by our technical experts. Consider this a primer on AI.

Here are the first four episodes of our podcast. Watch this page for new installments each month.