• Gordon Moore came up with Moore’s Law in 1965, suggesting that every two years; computational progress would become faster, smaller and more efficient.
  • Today, as we stand amidst the digital revolution that has fundamentally altered the foundational concepts of the electronics industry, the hunger for more capabilities and higher performance is driving innovation for the chip design industry.
  • This has also created a new class of challenges that chip designers must explore and overcome.
  • With the application of artificial intelligence (AI), semiconductor and systems companies can not only design better chips, but also speed time-to-market and save cost.
  • The world’s leading semiconductor companies will spend $300 million on internal and third-party AI tools for designing chips in 2023, and that number will grow by 20% annually for the next four years to surpass $500 million in 2026.
  • For advanced chip design, AI is the game changer
  • As we progress into a more digitally connected world, the demand for next-generation chips are on the rise.
  • Today, computing systems must process data and perform complex calculations at high speeds to support hyper connected devices such as Smartphone, wearable devices, autonomous vehicles, and a plethora of other electronic gadgets that we use every-day.
  • Artificial intelligence and big data are transforming the world around us, as they are transforming the way we think about electronic design automation (EDA).
  • EDA is the backbone of chip design – it encompasses the software, hardware and IP that chip designers use to design cutting-edge semiconductors.
  • The latest innovation in EDA is the integration of artificial intelligence into the software.
  • This is a game changer because it boosts engineering productivity and shrinks time to market, both of which are critical considerations in chip design.
  • AI helps users with automated, intelligent design insights and offers the ability to greatly scale engineering team productivity.
  • Previously, when a chip was taped out, the valuable data was deleted to make way for the next project.
  • There are valuable learnings in the legacy data, and today with the application of AI, it has become easy for engineering teams to access these learnings and apply them to future designs.
  • This enables delivery of optimal engineering productivity and ultimately more predictable, higher-quality product outcomes.


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