Pure AI-Powered
cloud-native PCB design

Yes. Automation was possible thanks to Reinforcement Learning (RL), a subfield of Machine Learning (ML). RL is particularly suited for decision-making problems: board games such as Chess or Go, logistics, mobility, or PCB routing. At InstaDeep, we deploy RL systems in the real world and work closely with hardware partners such as Nvidia and Intel. After a rigorous technical evaluation, Nvidia upgraded us to preferred partner, the highest status possible. On top of that, we are part of Intel’s AI Builder Program. Users can access the intermediary routing solutions. The user sees how the AI system progressively increases the quality of its routing as it finds out how to solve the user’s board.

InstaDeep has built credibility in AI circles by innovating in Machine Learning (ML) and Reinforcement Learning (RL), specifically on how to find good solutions to NP-Hard problems. InstaDeep has a track record of publishing cutting-edge AI research in that specific domain at top AI conferences such as NeurIPS, ICML, and ICLR (See our papers). InstaDeep also has expertise in productizing ML and RL research. DeepPCB™ is an example but one can also mention DeepPack™ or read this blog post about InstaDeep and Deutsche Bahn collaboration in a new tab”>InstaDeep’s long-term collaboration with Deutsche Bahn, which is on its way to revolutionizing the railway industry in Germany, using Deep Reinforcement Learning.

InstaDeep will never share or sell any of your data or intelligence to any outside entity. Our sole goal is to accelerate your PCB product development cycle.

This is exactly the problem that DeepPCB™ is addressing. Current auto-routers often do not get the job done because the problem that has all real-life constraints is too difficult for the usual heuristics that current software tools rely on. As the routing problem carries heavy constraints, optimization solvers cannot find solutions in reasonable time. Finally, DeepPCB™ leverages past experience through learning, as opposed to heuristics or solvers who need to start from scratch for each new instance that has to be solved.