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32 DESIGN007 MAGAZINE I JANUARY 2018 selection and guidance. The results are far more amenable to professional PCB designers and engineers (Figures 7 and 8). This current generation of routing automa- tion is a great step forward, providing speed for designers while allowing them to maintain quality and control of routing. But in my opin- ion, it won't be enough to carry us into the future of IoT. Neural net-based topological routing engines need to be way more powerful. But the only way we can improve them to the point of useful results for every desktop, is through deep learning. Again, coming from a com- plete layman, deep learning is a fancy term for expanding the data set that teaches the artificial intelligence engine called a deep neural network, and applying that expansive set of stimuli and responses to develop the software (or machine's) behavior. The real question here is, what if the neural net routing automation had a practically unlimited set of PCB designs to train it? What if, each time you design a new PCB in your EDA tool, the EDA tool could learn your moves and begin to help you by anticipating how the board could be routed, or even how moving some parts on the board (within your constraints, of course) could drastically reduce routing time and layer count? What if you could leverage and contrib- ute (voluntarily) to a hive-mind of engineers and PCB designers anonymously to train the world's greatest routing engine? There'd be no excuse left for not designing the next imperative device! DESIGN007 References 1. Ericsson 2. IBM 3. Cisco 4. IEEE Tech Talk Ben Jordan is senior manager of product and persona marketing. He is a computer systems and PCB engineer with over 20 years of experience in embedded systems, FPGA, and PCB design. Figure 7: User-guided routing automation (Source: Altium LLC 2017). Figure 8: User-guided automation results (Source: Altium LLC 2017).

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