Artificial intelligence (AI) and machine learning (ML) are not only transforming software applications, but also hardware design. In this article, you will learn how you can use AI and ML for hardware design and what benefits they can bring.
Hardware design is a complex and time-consuming process that involves many trade-offs between performance, power, cost, reliability, and functionality. AI and ML can help hardware designers to optimize these trade-offs, automate tedious tasks, explore new design spaces, and discover novel solutions. AI and ML can also enable hardware design for emerging domains, such as neuromorphic computing, quantum computing, and edge computing.
Hardware design can be classified into two categories. First is the IC design and second is the Electronics design using the ICs to design the Printed Circuit boards. Both of them need extensive use of tools. AI/ML helps if the tools use these two. The trouble is the design rules that primarily drive the complex design tend to be closely held by the designers. For ML/AL you need these data to build into the tools. But the situation is orthogonal and needs collaboration with different companies to make them share the closely held secrets to help the community. This is going to be a big challenge. If this can be achieved the design time and revisions can be drastically reduced to save time and money.
AI and ML can be used for hardware design at different stages of the design cycle and levels of abstraction. For example, AI and ML can be used for high-level synthesis to generate efficient and optimized hardware architectures from software specifications. Additionally, AI and ML can be used for logic synthesis to reduce area, delay, and power consumption of the logic circuit. AI and ML can also be used for physical design to improve the quality of placement and routing, minimize wire length and congestion, and avoid design violations. Finally, AI and ML can be used for verification and testing to generate effective test cases, identify faults and bugs, and improve coverage and efficiency of the process.
From my 3 decades plus experience in hardware design. Primary challenges have been in engineering the designs for large-volume production (both in VLSI and in PCBAs). CAD tool vendors create generic rules for checking the design as the design with tools progresses. However, experience is needed for High Speed, High power, and EMC/EMI-compliant designs which most companies do hold as their family silver and rarely made public. HP was on organization that had one of the best design guidelines that can be integrated into tools. I used those 200+ page books when I worked for a company that was JV with HP. If these kinds of golden design rules which are public can be brought into the ML/AI framework the efficiency in design will go up multifold
Utilizing AI and ML for hardware design can be incredibly advantageous, such as reducing design time and cost through automation and accelerating design tasks. Moreover, it can improve the design quality and performance by optimizing design parameters and metrics to find the optimal trade-offs. Additionally, AI and ML can enhance design creativity and innovation by exploring new solutions that may not be obvious to human designers. Furthermore, it is capable of adapting to design changes and requirements by learning from feedback and data to update the design accordingly.
Assuming we have a set of tools that use globally available knowledge and use them as the basis for ML/AI-enabled design tools. advantages are; 1. No need for experienced engineers to be involved because they have the "Tribal Knowledge" which is closely held. 2. Avoiding an error during the design reduces the development time and reduces the number of revisions above all the development costs. 3. for example if the semiconductor vendors have a repository of real-time availability many of manufacturing delays can be avoided. But this is easier said than done with Semiconductor companies reducing the inventory holds prices vary depending on desperation! 4. Todays education engineers lack basic and ML/AI can help in bridging that gap
The use of AI and ML for hardware design poses some challenges, such as finding the appropriate models and methods that suit the specific problem and domain, as well as ensuring the reliability and robustness of the solutions. Moreover, there is the need to integrate AI and ML tools with existing hardware design tools, while balancing the trade-offs between the overhead and benefit of AI and ML. Additionally, it is important to ensure compatibility and interoperability between the tools, as well as provide explainable and interpretable results and decisions.
AI/ML usage will depend on the tools vendors and how they integrate ML/AI. ML implementation calls for training and we need data for that. However, engineering a product is an art and many OEMs keep the rules they use as Intellectual Property or secret. Tool vendor-provided ML/AI can solve generic and mundane issues, but not challenging issues. So challenge will be how complex issues that differentiate the products are brought into the framework of the tools. Till these issues are addressed ML/AI will have only basic success.
If you are interested in learning more about using AI and ML for hardware design, there are many resources available. For example, you can find online courses and tutorials to teach the basics and advanced topics of AI and ML, as well as their applications and examples for hardware design. Additionally, there are books and papers that provide comprehensive and in-depth knowledge on AI and ML for hardware design, as well as the latest research and developments in the field. Furthermore, blogs and podcasts share the experiences and opinions of experts and practitioners on AI and ML for hardware design, as well as the trends and challenges in the industry. Finally, events and communities offer opportunities to network with other hardware designers, learn from best practices, and interact with AI and ML enthusiasts.