For the sake of simplicity, let’s assume that training takes place in the cloud, and inference will take place at the edge or in-device. As we’ve described, ML and DL are data-centric disciplines.

Machine learning and deep learning are showing a sharp growth trajectory in many industries. Even the semiconductor industry, which generally has resisted this technology, is starting to changing its ...

That means AI/ML/DL algorithms can be used in EDA tools to manage bigger designs, and to spot potential weaknesses or flaws in those designs. “As the designs get bigger, the amount of data gets ...

One thing that impressed me, which didn’t get much press, was the number of rich conversations from IBM customers on how they were using Power Systems to help their AI, ML and DL workflows.