Abstract: A method of performing automatic tuning on a deep learning model includes: utilizing an instruction-based learned cost model to estimate a first type of operational performance metrics based on a tuned configuration of layer fusion and tensor tiling; utilizing statistical data gathered during a compilation process of the deep learning model to determine a second type of operational performance metrics based on the tuned configuration of layer fusion and tensor tiling; performing an auto-tuning process to obtain a plurality of optimal configurations based on the first type of operational performance metrics and the second type of operational performance metrics; and configure the deep learning model according to one of the plurality of optimal configurations.

Abstract: A rugged heterojunction bipolar transistor (HBT) power device and the optimal design method thereof are disclosed. By combining the epitaxial layer structure design (embedded emitter ballasting resistor and high breakdown voltage) and power cell design (clamping diodes and base ballasting resistor optimization), HBT power devices with excellent power performance and high ruggedness can be obtained.