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Electronic design has evolved over the years to provide methods for optimizing power, space, and energy needs for the most demanding market applications in areas including hyperscale computing, consumer, 5G communications, automotive, mobile, aerospace, industrial, and healthcare. Because of these design innovations, electronics have become an essential part of our consumer and industrial daily lives. Sustainability is becoming an important consideration of electronics design and has even risen to the level of corporate governance.
Our company and our customers have heard this rallying cry for increased sustainability, and we are significantly enhancing our design projects to do our part to invest in the health of the planet. The pressure is on for organizations to create and adopt long-term sustainability goals and environmental, social, and governance (ESG) goals.
Key to decreasing e-waste and increasing sustainability is to design each component of the product right from the very start to extend the product life cycle. We call this intelligent system design, and many electronics companies are using this technology to create products with a lower carbon footprint.
Artificial Intelligence (AI) and machine learning (ML) are essential to the advancements made in intelligent system design. They are used to ensure that mistakes are not made in the electronic design automation (EDA) processes for complex electronic systems. And they can find opportunities to save power that might elude human designers.
AI is employed in almost every design tool we offer for the entire electronics design chain, from chip design to chip packaging to boards and to systems. AI helps deliver the next generation of power and energy efficiency, accurately predicting power early in the design process. AI helps engineers identify activities that can be eliminated, producing designs with the bare minimum of power required to perform a given function.
As the latest chips grow in size and complexity, a vast amount of design data is generated during verification and implementation. The Cadence Joint Enterprise Data and AI (JedAI) Platform harnesses this design data in an open, AI-driven, large-scale data analytics environment optimized for massive, heterogeneous, structured, and unstructured EDA data. By using the Cadence JedAI Platform, designers can quickly identify the most critical power, performance, and area (PPA) objectives and design bottlenecks, resulting in faster design closure with fewer engineering resources. Now designers can use AI-driven optimization and debug to create multiple designs in parallel with fewer engineers.
Skycore Semiconductors employed the Cadence Cloud Portfolio of design tools to create a high-voltage switched-capacitor microchip technology that can significantly reduce the size, price, and energy loss of power converters, thereby strengthening the transition towards a sustainable world powered by electricity.
Lotus Microsystems designed a power converter with 70% less size and 50% less weight than the state-of-the-art miniature power converter using AI-assisted design tools. They employed eco-friendly materials to drive product development in the direction of a more sustainable future in conjunction with United Nations (UN) sustainability goals.
By using pre-designed IP components, design teams can push the boundaries to achieve the lowest power impact in the industry while retaining significant performance advancement. For example, the Tensilica® HiFi 1 digital signal processor (DSP) extends the duration of voice communication and music playback, allowing always listening to voice commands with minimal impact on battery life. This processor for a greener world enables small form factor, low-cost consumer and mobile devices, as well as automotive and industrial devices.
Robust computational fluid dynamics (CFD) software uses AI techniques to simulate the behavior of fluids and their thermodynamic properties. Among other uses, this software has been used by Pipistrel Vertical Solutions to redesign for shorter aircraft takeoff and landing, reduced energy consumption for increased flight ranges, and noise reduction.
The data center construction and retrofitting market is growing rapidly, resulting in an incredible focus on power and energy efficiency. Data center managers need to both set aggressive emissions and energy reduction goals and then create the measurements to make sure they will meet those goals, which they can report back to investors, government agencies, and other interested parties. By creating a digital twin, a physics-based simulation of an entire data center, managers can make informed decisions about cutting costs, increasing energy efficiency, and safeguarding reliability to meet service level agreement (SLA) requirements. Response to requests can be evaluated quickly and factually while sustainability goals are measured.
These are just a few examples of the AI and ML techniques that are used throughout the electronic design process to provide better, more predictable, and more sustainable outcomes. Tools now suggest solutions to common problems that might otherwise take design teams weeks or months to evaluate. These suggestions help the electronics industry design products that will last longer, take up less space, require fewer valuable resources, and lead us to a sustainable future.