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: In some embodiments, the present disclosure relates to a device having a semiconductor substrate including a frontside and a backside. On the frontside of the semiconductor substrate are a first source/drain region and a second source/drain region. A gate electrode is arranged on the frontside of the semiconductor substrate and includes a horizontal portion, a first vertical portion, and a second vertical portion. The horizontal portion is arranged over the frontside of the semiconductor substrate and between the first and second source/drain regions. The first vertical portion extends from the frontside towards the backside of the semiconductor substrate and contacts the horizontal portion of the gate electrode structure. The second vertical portion extends from the frontside towards the backside of the semiconductor substrate, contacts the horizontal portion of the gate electrode structure, and is separated from the first vertical portion by a channel region of the substrate.

Abstract: Provided herein are devices and systems comprising an illumination module configured to provide a source light to an optical interference module, which converts the source light to a line of light and processes light signal; an interference objective module, which handles light from the optical interference module and processes light signal generated from a sample; a two-dimensional camera configured to receive a backscattered interference signal from the sample, and a data processing module which processes the interference signal into an image.

Abstract: Provided herein are devices and systems comprising an illumination module configured to provide a source light to an optical interference module, which converts the source light to a line of light and processes light signal; an interference objective module, which handles light from the optical interference module and processes light signal generated from a sample; a two-dimensional camera configured to receive a backscattered interference signal from the sample, and a data processing module which processes the interference signal into an image.

Abstract: An intelligent LED lamp group control device using an existing wall switch for controlling a group of LED lamps includes an AC power converter, an interface for intelligent control using an interface circuit for upgrading the existing wall switch for scene group control. The device further includes a night lamp control input port, an emergency operation switch, an external set control box connector, a wall switch input unit, a control configuration set interface, a power failure battery input port and a night lamp and RS-485 serial port. The device enables one single controller to achieve control of and provide power supply to multiple LED lamps, and uses a communication control interface for converting the power supply originally provided to the LED lamps into a logic control signal so that the wiring layout and location of the original lighting control wall switch can be used.