Electronic Design Automation Industry

Electronic Design Automation Industry
           Understanding the development of various technologically-related industries is essential for every IT professional. It allows him to have a good appreciation of the current and the future market that requires his own skillset and improve his own abilities depending on the demands of the situation. In this article, I would like to discuss the learnings that I had with Lecture 9 of our learning series regarding the Electronic Design Automation (EDA) Industry. This includes an overview of the industry as discussed by the speakers in the lecture, the skillsets needed in this domain, the common workflows, and other pertinent information. All in all, I believe that having an in-depth understanding of one's market is essential not only to be 'market-ready' for every professional but also to continuously improve and thrive in a competitive market like data science. 
The Electronic Design and Automation Industry
           Although not a totally new industry, the EDA is one of the booming market segments regarding data science. In general, this industry refers to the segment composed of systems, hardware, and software with the overall goal of improving and assisting the development of circuit boards, electronic chips, and other devices. These devices are then used for almost, if not all, electronic devices today, including mobile phones, laptops, and even gadgets, to name a few. In relation to the use of Artificial Intelligence (AI) and machine learning in EDA, the discussion showed that its primary purpose is to create cost-effective and innovative ways of producing electronic chips and devices that would be able to meet the ever-increasing need for quality and quantity. 
Data Science Skills
           Given the increasing need for innovation and demand for newer chips, producing new printed circuit boards and chips requires various data science skills. 
For example, one of the primary skills discussed in the lecture is machine learning and AI in designing and production. One of the examples given by the lecturer in the video was Synopsys' integration of various digital and non-digital systems for developing new silicon chips today. They noted that machine learning has to combine various elements such as IP, Design, silicon production, and verification and security, to name a few. In this domain, machine learning has to recognize patterns and trends in the production process and make it more cost-efficient to meet the strict quality and increasing quantity required by large companies like Apple, Samsung, and Qualcomm, to name a few. 
Aside from developing machine learning systems and software, every data science professional must have another essential skill when working in the field of EDA: determining statistical trends and patterns. This is important whether in the development process or in the determination of production quantity.
On the one hand, using statistical analysis is essential for improving the EDA by predicting market growth, anticipating challenges, and improving the market capability of any company. One example that I found interesting in the lecture was the prediction of Sir Gordon Moore about the boom in the silicon market as early as 1965. Although there were recent updates in the 'doubling speed' of the demand for transistors, Sir Moore's prediction has proven to still be highly applicable and authoritative decades after he made it, which gave rise to Moore's Law. Similarly, the determination of statistical trends and patter is essential in the development process. 
On the other hand, a statistical determination is also useful and helpful in the development of machine learning. Although indirect, data scientists should be able to program AI with the capacity to adjust their calculations frequently bas...