Abstract: A method of forming a semiconductor device includes forming active regions, forming S/D regions, forming MD contact structures and forming gate lines resulting in corresponding transistors that define a first time delay circuit having a first input configured to receive a first clock signal and having a first output configured to generate a second clock signal from the first clock signal; and corresponding transistors that define a second time delay circuit having a second input configured to receive the second clock signal and having a second output configured to generate a third clock signal from the first clock signal; forming a first gate via-connector in direct contact with the first gate line atop the first-type active region in the first area; and forming a second gate via-connector in direct contact with the second gate line atop the second-type active region in the second area.

Abstract: An integrated circuit (IC) device includes at least one circuit having an input and an output, and an output connector electrically coupled to the output. The circuit further includes a plurality of transistors electrically coupled with each other between the input and the output. The output is in a first metal layer. The output connector includes a first conductive pattern in the first metal layer, and a second conductive pattern in a second metal layer different from the first metal layer. The second conductive pattern electrically couples the output to the first conductive pattern.

Abstract: Described herein are classifiers that are used to determine whether or not to partition a block in frame during prediction using recursive partitioning. Blocks of training video frames are encoded using recursive partitioning to generate encoded blocks. Training instances are generated for the encoded blocks that include values of features extracted from each encoded block and a label indicating whether or not the encoded block is partitioned into smaller blocks in the recursive partitioning. The classifiers are trained for different block sizes using the training instances associated with the block size as input to a machine-learning process. When encoding frames of a video sequence, the output of the classifiers determines whether input blocks are partitioned during encoding.

Abstract: Described herein are classifiers that are used to determine whether or not to partition a block in frame during prediction using recursive partitioning. Blocks of training video frames are encoded using recursive partitioning to generate encoded blocks. Training instances are generated for the encoded blocks that include values of features extracted from each encoded block and a label indicating whether or not the encoded block is partitioned into smaller blocks in the recursive partitioning. The classifiers are trained for different block sizes using the training instances associated with the block size as input to a machine-learning process. When encoding frames of a video sequence, the output of the classifiers determines whether input blocks are partitioned during encoding.

Abstract: Described herein are classifiers that are used to determine whether or not to partition a block in frame during prediction using recursive partitioning. Blocks of training video frames are encoded using recursive partitioning to generate encoded blocks. Training instances are generated for the encoded blocks that include values of features extracted from each encoded block and a label indicating whether or not the encoded block is partitioned into smaller blocks in the recursive partitioning. The classifiers are trained for different block sizes using the training instances associated with the block size as input to a machine-learning process. When encoding frames of a video sequence, the output of the classifiers determines whether input blocks are partitioned during encoding.

Abstract: Described herein are classifiers that are used to determine whether or not to partition a block in frame during prediction using recursive partitioning. Blocks of training video frames are encoded using recursive partitioning to generate encoded blocks. Training instances are generated for the encoded blocks that include values of features extracted from each encoded block and a label indicating whether or not the encoded block is partitioned into smaller blocks in the recursive partitioning. The classifiers are trained for different block sizes using the training instances associated with the block size as input to a machine-learning process. When encoding frames of a video sequence, the output of the classifiers determines whether input blocks are partitioned during encoding.