Tech Companies Leverage Artificial Intelligence to Increase Engineering Productivity

During my 25+ year career serving and partnering with customers in the semiconductor industry, I have watched them weather through transitions, great and small. Using ingenuity, innovation, and strategic partnerships, they’ve overcome industry disruptions and technology transitions, maintaining—and often advancing—their competitive place in the industry.

A Sellers’ Market

It’s no secret that the law of supply and demand is wreaking havoc on companies that are looking to fill jobs but from an ever-shrinking applicant pool. A recent survey from the Institute of Supply Management found that demand for workers remained “hypercompetitive” and that there were not enough qualified workers available.

Exacerbating the issue are the costly and competitive lengths that companies will go to recruit new talent: High salaries, significant sign-on bonuses, stock options, remote and flexible work schedules, unlimited PTO, and other buzzy perks. For example:

• Business leaders are offering higher pay and more competitive benefits to combat rising resignation rates and attract new hires, according to research from the Society for Human Resource Management. More than half of organizations report that beyond normal yearly increases, they are offering higher starting salaries and wages than last year.
• The increased use of select cash compensation programs in the new “war for talent” includes sign-on bonuses, retention bonuses, and special incentive/bonus programs, according to Korn Ferry in its “Compensation Practices and Salary Increase Projections for 2022” study.
• Job postings that advertised incentives like sign-on bonuses doubled from July 2020 to July 2021, according to Indeed.com.

While the competition for talented employees varies by industry, nowhere is it more intense than in the semiconductor ecosystem as companies scramble to address the explosive growth in Cloud, 5G, Automotive, IoT, and Hyperscaler markets, to name a few. These markets continue to have an unquenchable thirst for the most advanced semiconductor technology and the skilled labor necessary to develop the underlying technology.

The electronics industry now has two significant challenges: The traditional need to deliver differentiated products to the market and the need to hire the talent necessary to deliver those products on time and in a cost-effective manner. Before we address these challenges, let’s review the role of Electronic Design Automation (EDA) in supporting the electronics ecosystem. 

Chip designer productivity is in the DNA of EDA and foundational to our industry’s name. Think back to the early days of EDA when engineers drew circuits by hand and then checked the physical implementation manually. You could call that EDA 1.0.

Verilog—the hardware description language (HDL) used to model electronic systems—helped  automate the design, implementation, and verification process, ushering in EDA 2.0. These software solutions led to new functionality and exponential scalability, fueling the growth of electronic and allowing designers to produce more in the same timeframe. We have seen that the companies that embraced these new technologies first had a competitive advantage as they could deliver differentiated products to market sooner. 

EDA 3.0

Are we now on the cusp of another technology transition that will enable semiconductor companies to continue to scale their productivity? EDA companies are leveraging artificial intelligence (AI) and machine learning (ML) capabilities to develop new solutions with step-function improvements in productivity and results. Semiconductor companies can now equip their existing design engineering teams with these new EDA solutions, increasing their engineers’ productivity and accelerating their ability to deliver differentiated products to market. While it won’t totally replace the need to hire more engineers, it will release some of the pressure on the urgent problem of hiring engineers in this ultra-aggressive job market.

As my colleague Kam Kittrell, Vice President in the Cadence Digital & Signoff Group, recently wrote, “AI in EDA isn’t just making things a bit better. It is fundamentally changing what can be achieved, what can be designed, how teams can scale, what talent is required, and what the operating model of an R&D organization requires.”

In his recently published article, AI Unleashes Chip Designer Productivity, Kittrell documents how the recently launched Cadence® Cerebrus Intelligent Chip Explorer is becoming a productivity game-changer for our customers. In fact, it’s a revolutionary product that provides both optimized PPA and significant productivity benefits. Companies need fewer engineering resources versus traditional methodologies, providing better results in less time. Ultimately, it allows companies to deliver more complex, differentiated products to the market on time. 

This EDA-pioneered productivity breakthrough and others being deployed couldn’t come at a better time, given the brutal competition for recruiting new technical talent. These AI and ML advances give chip companies and their HR teams some alternative to the frenzied hiring landscape while providing higher productivity and better ROI from the talented engineering teams already in place.