Abstract: This application relates to a multi-layer convolution operation. The multi-layer convolution operation is optimized for a vector processing unit having a number of data paths configured to operate on vector operands containing a number of elements processed in parallel by the data paths. The convolution operation specifies a convolution kernel utilized to filter a multi-channel input and generate a multi-channel output of the convolution operation. A number of threads are generated to process blocks of the multi-channel output, each block comprising a set of windows of a number of channels of the multi-channel output. Each window is a portion of the array of elements in a single layer of the multi-channel output. Each thread processes a block in accordance with an arbitrary width of the block, processing a set of instructions for each sub-block of the block having a well-defined width, the instructions optimized for the vector processing unit.

Abstract: This application relates to performing fully-connected inferences using a convolutional neural network. A method includes receiving a two-dimensional input matrix that includes a plurality of elements. The method further includes identifying a two-dimensional weight matrix corresponding to the two-dimensional input matrix, where the two-dimensional weight matrix includes a plurality of weight values. The method further includes transposing a first column of the two-dimensional weight matrix and storing the transposed first column of the two-dimensional weight matrix in a first register having a first length corresponding to the transposed first column. The method further includes generating a first output element by performing a first dot product operation using a first row of the two-dimensional input matrix and the transposed first column. Finally, the method includes storing the first output element in a first row of a two-dimensional output matrix.

Abstract: This application relates to classifying information using a fully-connected layer of a convolutional neural network. A method for classifying information using a fully-connected layer of a convolutional neural network includes calculating a first partial output for a first block of elements by performing a dot product operation using a first row of elements of the first block of elements and a first weight block, where the first row of elements of the first block of elements corresponds to a first batch of elements. The method further includes generating a first output element using the first partial output for the first block of elements and at least one other partial output corresponding to the first batch of elements.

Abstract: This application relates to a multi-layer convolution operation. The multi-layer convolution operation is optimized for a vector processing unit having a number of data paths configured to operate on vector operands containing a number of elements processed in parallel by the data paths. The convolution operation specifies a convolution kernel utilized to filter a multi-channel input and generate a multi-channel output of the convolution operation. A number of threads are generated to process blocks of the multi-channel output, each block comprising a set of windows of a number of channels of the multi-channel output. Each window is a portion of the array of elements in a single layer of the multi-channel output. Each thread processes a block in accordance with an arbitrary width of the block, processing a set of instructions for each sub-block of the block having a well-defined width, the instructions optimized for the vector processing unit.

Abstract: Receiving an instruction indicating first and second operands. Each of the operands having packed data elements that correspond in respective positions. A first subset of the data elements of the first operand and a first subset of the data elements of the second operand each corresponding to a first lane. A second subset of the data elements of the first operand and a second subset of the data elements of the second operand each corresponding to a second lane. Storing result, in response to instruction, including: (1) in first lane, only lowest order data elements from first subset of first operand interleaved with corresponding lowest order data elements from first subset of second operand; and (2) in second lane, only highest order data elements from second subset of first operand interleaved with corresponding highest order data elements from second subset of second operand.

Abstract: Receiving an instruction indicating first and second operands. Each of the operands having packed data elements that correspond in respective positions. A first subset of the data elements of the first operand and a first subset of the data elements of the second operand each corresponding to a first lane. A second subset of the data elements of the first operand and a second subset of the data elements of the second operand each corresponding to a second lane. Storing result, in response to instruction, including: (1) in first lane, only lowest order data elements from first subset of first operand interleaved with corresponding lowest order data elements from first subset of second operand; and (2) in second lane, only highest order data elements from second subset of first operand interleaved with corresponding highest order data elements from second subset of second operand.

Abstract: A loop remainder mask instruction indicates a current iteration count of a loop as a first operand, an iteration limit of a loop as a second operand, and a destination. The loop contains iterations and each iteration includes a data element of the array. A processor receives the loop remainder mask instruction, decodes the instruction for execution, and stores a result of the execution in the destination. The result indicates a number of data elements of the array past an end of a preceding portion of the array that are to be handled separately from the preceding portion, the end of the preceding portion being where the current iteration count is recorded.

Abstract: Receiving an instruction indicating first and second operands. Each of the operands having packed data elements that correspond in respective positions. A first subset of the data elements of the first operand and a first subset of the data elements of the second operand each corresponding to a first lane. A second subset of the data elements of the first operand and a second subset of the data elements of the second operand each corresponding to a second lane. Storing result, in response to instruction, including: (1) in first lane, only lowest order data elements from first subset of first operand interleaved with corresponding lowest order data elements from first subset of second operand; and (2) in second lane, only highest order data elements from second subset of first operand interleaved with corresponding highest order data elements from second subset of second operand.

Abstract: A technique to accelerate range detection in a spline calcuation. In one embodiment, an instruction and corresponding logic are provided to perform range detection within a computer or processor.

Abstract: Receiving an instruction indicating first and second operands. Each of the operands having packed data elements that correspond in respective positions. A first subset of the data elements of the first operand and a first subset of the data elements of the second operand each corresponding to a first lane. A second subset of the data elements of the first operand and a second subset of the data elements of the second operand each corresponding to a second lane. Storing result, in response to instruction, including: (1) in first lane, only lowest order data elements from first subset of first operand interleaved with corresponding lowest order data elements from first subset of second operand; and (2) in second lane, only highest order data elements from second subset of first operand interleaved with corresponding highest order data elements from second subset of second operand.

Abstract: A video processing device may comprise a video processing logic to control the enhancement operations performed on the video processing device. The video processing logic may determine a short term frame rate average value in response to receiving a plurality of video frames. Further, the video processing logic may generate a derivative of the short term frame rate using the short term frame rate value. The video processing logic may then activate monitoring of a processor usage if the derivative of the short term frame rate is below a first threshold value. The video processing logic may then reduce the performance of rendering of the plurality of video frames if a processor usage average value is above a second threshold. While restoring the performance, the video processing logic may restore the enhancement operations in steps after determining that processor resources are available.

Abstract: A technique to accelerate range detection in a spline calcuation. In one embodiment, an instruction and corresponding logic are provided to perform range detection within a computer or processor.