Celus uses Machine Learning, Reinforcement Learning and Supervised Learning for converting the input parameters into actual hardware designs for electronics.

With the increasing use of AI, more and more start-ups are focusing on innovative ideas that will bring technology development to the next level. Celus found a unique way to apply Artificial Intelligence and Machine Learning in the field of automating electronics engineering, we call it augmented model-based hardware development. We were thrilled to attend the appliedAI Meetup held at the Munich Google Office on the 5th of April 2019. To drive forward real-word applications of AI, the appliedAI Initiative together with Google organized that meet up to bring together a local community of start-ups, researchers and corporates discussing latest developments in AI. Celus was part of this event in the Startup exhibition among others like Statice, Deevio, Geospin and Gini.

The appliedAI initiative is part of the UnternehmerTUM ecosystem. UnternehmerTUM, one of the largest non-profit innovation and entrepreneurship centers in Europe, joined forces with leading public sector, industry and tech players with the applied AI initiative.

One can say that artificial intelligence is a term that inspires both excitement and confusion in people’s minds. Nowadays AI is an important part of our lives, it can be seen in many every day products from intelligent personal assistants in smartphones, driverless cars and special purpose robots for emailing, navigation, web search and social media or even in some of the fields like marketing and HR.

Artificial intelligence makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Currently it enables Celus to integrate information more effectively, analyze data, and use the resulting insights to improve decision-making for our automated electronics engineering. Most AI examples that we hear about today rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in that data. That is also an important use-case of AI for Celus. In order to improve our automation algorithms, there are a lot of design rules to be taken into consideration. This means there is a huge solution space of different configurations that need to be analyzed. These analyses heavily influence the output quality and computational effort of our model-based design algorithms. Celus uses Machine Learning, Reinforcement Learning and Supervised Learning to estimate those needed configurations and other parameters for converting the input specification of our customers into actual hardware designs for electronics.

We will see many more news and inventions in the AI domain in 2019, the world is changing rapidly, and a new era of technology is arising.

Stay tuned for more news!