Abstract: Techniques are disclosed relating to instruction set architecture support for matrix manipulations. In disclosed embodiments, front-end circuitry is configured to fetch and decode a matrix multiply instruction for execution, including to encode a given matrix input operand of the matrix multiply instruction to identify one or more vector registers defined according to an instruction set architecture. In some embodiments, datapath circuitry is configured to execute the matrix multiply instruction, where during execution of the instruction, the one or more vector registers corresponding to the given matrix operand are mapped within the datapath circuitry to at least two dimensions of the given matrix operand. In some embodiments, power management circuitry is configured to, during execution of the instruction, operate at least a portion of the front-end circuitry in a reduced-power mode.

Abstract: Techniques are disclosed relating to dedicated power function circuitry for a floating-point power instruction. In some embodiments, execution circuitry is configured to execute a floating-point power instruction to evaluate the power function xy as 2y log2x. In some embodiments, base-2 logarithm circuitry is configured to evaluate a base-2 logarithm for a first input (e.g., log2 x) by determining coefficients for a polynomial function and evaluating the polynomial function using the determined coefficients and the first input. In some embodiments, multiplication circuitry multiplies the base-2 logarithm result by a second input to generate a multiplication result. In some embodiments, base-2 power function circuitry is configured to evaluate a base-2 power function for the multiplication result. Disclosed techniques may advantageously increase performance and reduce power consumption of floating-point power function operations with reasonable area and accuracy, relative to traditional techniques.

Abstract: Aspects and features include using a virtual disk image to improve computational performance when applying a software patch. Compressed extents within a stored disk image are detected. The compressed extents are virtually reordered to form compressed forks within a virtual disk image and the compressed forks are selected for decompression based on code to be patched. A decompressed fork with the patch is virtually written to the same or another virtual disk image as an updated fork, and the virtual disk image is used to write to storage, either to overwrite the same stored disk image or to produce an updated, compressed disk image. In some examples, the virtual disk image is validated prior to writing to the compressed image by comparing an output hash from the compressed disk image with a known hash to validate the virtual disk image.

Abstract: Methods for lossy and lossless pre-processing of image data. In one embodiment, a method for lossy pre-processing image data, where the method may include, at a computing device: receiving the image data, where the image data includes a model having a mesh, the mesh includes vertices defining a surface, the vertices including attribute vectors, and the attribute vectors including values. The method also including quantizing the values of the attribute vectors to produce modified values, where a precision of the modified values is determined based on a largest power determined using a largest exponent of the values, encoding pairs of the modified values into two corresponding units of information. The method also including, for each pair of the pairs of the modified values, serially storing the two corresponding units of information as a data stream into a buffer, and compressing the data stream in the buffer.

Abstract: Aspects and features include using a virtual disk image to improve computational performance when applying a software patch. Compressed extents within a stored disk image are detected. The compressed extents are virtually reordered to form compressed forks within a virtual disk image and the compressed forks are selected for decompression based on code to be patched. A decompressed fork with the patch is virtually written to the same or another virtual disk image as an updated fork, and the virtual disk image is used to write to storage, either to overwrite the same stored disk image or to produce an updated, compressed disk image. In some examples, the virtual disk image is validated prior to writing to the compressed image by comparing an output hash from the compressed disk image with a known hash to validate the virtual disk image.

Abstract: Methods for lossy and lossless pre-processing of image data. In one embodiment, a method for lossy pre-processing image data, where the method may include, at a computing device: receiving the image data, where the image data includes a model having a mesh, the mesh includes vertices defining a surface, the vertices including attribute vectors, and the attribute vectors including values. The method also including quantizing the values of the attribute vectors to produce modified values, where a precision of the modified values is determined based on a largest power determined using a largest exponent of the values, encoding pairs of the modified values into two corresponding units of information. The method also including, for each pair of the pairs of the modified values, serially storing the two corresponding units of information as a data stream into a buffer, and compressing the data stream in the buffer.

Abstract: In an embodiment, a processor supports one or more compression assist instructions which may be employed in compression software to improve the performance of the processor when performing compression/decompression. That is, the compression/decompression task may be performed more rapidly and consume less power when the compression assist instructions are employed then when they are not. In some cases, the cost of a more effective, more complex compression algorithm may be reduced to the cost of a less effective, less complex compression algorithm.

Abstract: A method for encoding text includes grouping text as a sequence of bytes, the text comprising a string of characters, each byte corresponding to a character in the text. For each byte of the sequence of bytes: (a) each bit is processed from most significant bit to least significant bit to generate a context; and (b) a subsequent bit is predicted, using a prediction model, based on the context generated based on previously processed bits, prediction of the prediction model being a combination of predictions of a plurality of sub-models. An encoded bitstream is output based on the predicted bits. The encoded bitstream includes encoded data corresponding to the text.

Abstract: A processor is provided with a register file comprising a plurality of vector registers, and an execution core coupled to the register file, where the execution core is configured to execute a set of checksum instructions with a first checksum instruction to specify a first vector operand, a second vector operand, and a result vector operand, where the first vector operand is in a first vector register of the plurality of vector registers, the second vector operand is in a second register of the plurality of vector registers, and the result vector operand is to be written to a third vector register of the plurality of vector registers, and to execute the first checksum instruction, the execution core is configured to accumulate bytes from the first vector operand and the second vector operand into a first portion of the result vector operand and add the accumulated bytes from the first vector operand and the second vector operand to a second portion of the result vector operand to generate the second portion

Abstract: In an embodiment, a processor supports one or more compression assist instructions which may be employed in compression software to improve the performance of the processor when performing compression/decompression. That is, the compression/decompression task may be performed more rapidly and consume less power when the compression assist instructions are employed then when they are not. In some cases, the cost of a more effective, more complex compression algorithm may be reduced to the cost of a less effective, less complex compression algorithm.

Abstract: Methods for lossy and lossless pre-processing of image data. In one embodiment, a method for lossy pre-processing image data, where the method may include, at a computing device: receiving the image data, where the image data includes a model having a mesh, the mesh includes vertices defining a surface, the vertices including attribute vectors, and the attribute vectors including values. The method also including quantizing the values of the attribute vectors to produce modified values, where a precision of the modified values is determined based on a largest power determined using a largest exponent of the values, encoding pairs of the modified values into two corresponding units of information. The method also including, for each pair of the pairs of the modified values, serially storing the two corresponding units of information as a data stream into a buffer, and compressing the data stream in the buffer.

Abstract: The subject technology groups received data in data blocks having a predetermined number of bytes. For each received data block, a compressed data block is written to an output buffer. The compressed data block includes a mask block having a same number of bits as the predetermined number, and a subsequent block. The mask block includes in a same order as bytes within the corresponding data block, a zero corresponding to a zero-byte within the data block, and a one corresponding to each non-zero byte within the data block. The subsequent block includes non-zero bytes within the corresponding data block in a same order as the non-zero bytes within the data block.

Abstract: Compression techniques are described. In an embodiment, a first plane of sensor data is accessed, the first plane of sensor data is divided into a plurality of slices, each sample is encoded in each slice from the plurality of slices, where encoding a sample include computing a median based prediction for the sample, computing an error for the sample comprising a difference between the sample and the computed median based prediction, determining a context for the sample, selecting a model for the sample by using the determined context, and encoding the computed error by using the selected model.

Abstract: In an embodiment, a processor supports one or more compression assist instructions which may be employed in compression software to improve the performance of the processor when performing compression/decompression. That is, the compression/decompression task may be performed more rapidly and consume less power when the compression assist instructions are employed then when they are not. In some cases, the cost of a more effective, more complex compression algorithm may be reduced to the cost of a less effective, less complex compression algorithm.

Abstract: A processor is provided with a register file comprising a plurality of vector registers, and an execution core coupled to the register file, where the execution core is configured to execute a set of checksum instructions with a first checksum instruction to specify a first vector operand, a second vector operand, and a result vector operand, where the first vector operand is in a first vector register of the plurality of vector registers, the second vector operand is in a second register of the plurality of vector registers, and the result vector operand is to be written to a third vector register of the plurality of vector registers, and to execute the first checksum instruction, the execution core is configured to accumulate bytes from the first vector operand and the second vector operand into a first portion of the result vector operand and add the accumulated bytes from the first vector operand and the second vector operand to a second portion of the result vector operand to generate the second portion

Abstract: Methods for lossy and lossless pre-processing of image data. In one embodiment, a method for lossy pre-processing image data, where the method may include, at a computing device: receiving the image data, where the image data includes a model having a mesh, the mesh includes vertices defining a surface, the vertices including attribute vectors, and the attribute vectors including values. The method also including quantizing the values of the attribute vectors to produce modified values, where a precision of the modified values is determined based on a largest power determined using a largest exponent of the values, encoding pairs of the modified values into two corresponding units of information. The method also including, for each pair of the pairs of the modified values, serially storing the two corresponding units of information as a data stream into a buffer, and compressing the data stream in the buffer.

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: In an embodiment, a processor supports one or more compression assist instructions which may be employed in compression software to improve the performance of the processor when performing compression/decompression. That is, the compression/decompression task may be performed more rapidly and consume less power when the compression assist instructions are employed then when they are not. In some cases, the cost of a more effective, more complex compression algorithm may be reduced to the cost of a less effective, less complex compression algorithm.

Abstract: In an embodiment, a processor supports one or more compression assist instructions which may be employed in compression software to improve the performance of the processor when performing compression/decompression. That is, the compression/decompression task may be performed more rapidly and consume less power when the compression assist instructions are employed then when they are not. In some cases, the cost of a more effective, more complex compression algorithm may be reduced to the cost of a less effective, less complex compression algorithm.

Abstract: In an embodiment, a computation engine is configured to perform vector multiplications, producing either vector results or outer product (matrix) results. The instructions provided to the computation engine specify a matrix mode or a vector mode for the instructions. The computation engine performs the specified operation. The computation engine may perform numerous computations in parallel, in an embodiment. In an embodiment, the instructions may also specify an offset with the input memories, providing additional flexibility in the location of operands. More particularly, the computation engine may be configured to perform numerous multiplication operations in parallel and to accumulate results in a result memory, performing multiply-accumulate operations for each matrix/vector element in the targeted locations of the output memory.