Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...

Why floating point is important for developing machine-learning models. What floating-point formats are used with machine learning? Over the last two decades, compute-intensive artificial-intelligence ...

A technical paper titled “Application of Machine Learning in FPGA EDA Tool Development” was published by researchers at the University of Texas Dallas. “With the recent advances in hardware ...