Abstract: Compressed data is oftentimes beneficial for reducing the computing resources required, for example, to transmit and store data. The compression of data is particularly useful when dealing with sparse data (data that includes numerous zeros or near-zero values) and only non-zero values above a certain threshold have significance. When dealing with compressed data, oftentimes the data needs to be decompressed for processing (e.g., by deep learning networks or other applications configured to operate on sparse, or other uncompressed data). Instructions are disclosed for supporting the decompression of compressed data by a processing unit such as a CPU and GPU.

Abstract: Many computing systems process data organized in a matrix format. For example, artificial neural networks perform numerous computations on data organized into matrices using conventional matrix arithmetic operations. One such operation is the transpose operation. Techniques are introduced for storing a matrix in a compressed format that allows, for example, a transpose operation to be performed during decompression. Thus, by utilizing the introduced techniques, transformations of compressed matrices such transposition can be achieved in a more effective way. Parallel processing may also be used to more efficiently compress and/or decompress.

Abstract: Many computing systems process data organized in a matrix format. For example, artificial neural networks perform numerous computations on data organized into matrices using conventional matrix arithmetic operations. One such operation is the transpose operation. Techniques are introduced for storing a matrix in a compressed format that allows, for example, a transpose operation to be performed during decompression. Thus, by utilizing the introduced techniques, transformations of compressed matrices such transposition can be achieved in a more effective way. Parallel processing may also be used to more efficiently compress and/or decompress.

Abstract: Compressed data is oftentimes beneficial for reducing the computing resources required, for example, to transmit and store data. The compression of data is particularly useful when dealing with sparse data (data that includes numerous zeros or near-zero values) and only non-zero values above a certain threshold have significance. When dealing with compressed data, oftentimes the data needs to be decompressed for processing (e.g., by deep learning networks or other applications configured to operate on sparse, or other uncompressed data). Instructions are disclosed for supporting the decompression of compressed data by a processing unit such as a CPU and GPU.

Abstract: A subsystem configured to encode an RGBA8 data stream assembles sequences of four-byte groups from the data stream. The subsystem decorrelates the red and blue channels, and computes a difference between each four-byte group and an anchor value. The anchor is encoded at full value. The subsystem then assigns each group a five-bit header based on the number and location of non-zero bytes and on the data content of the non-zero bytes within the group. The subsystem favors zero valued bytes. Thus, when a group includes only zero valued bytes, the header is sufficient to encode the group; no data bits are necessary. Further, two successive groups of zero-valued bytes may be encoded as a single header with no data bits, achieving further data reduction. Finally, the subsystem concatenates all the headers with associated data to yield the source data stream compressed to some ratio, e.g. four-to-one.

Abstract: A subsystem configured to encode an RGBA8 data stream assembles sequences of four-byte groups from the data stream. The subsystem decorrelates the red and blue channels, and computes a difference between each four-byte group and an anchor value. The anchor is encoded at full value. The subsystem then assigns each group a five-bit header based on the number and location of non-zero bytes and on the data content of the non-zero bytes within the group. The subsystem favors zero valued bytes. Thus, when a group includes only zero valued bytes, the header is sufficient to encode the group; no data bits are necessary. Further, two successive groups of zero-valued bytes may be encoded as a single header with no data bits, achieving further data reduction. Finally, the subsystem concatenates all the headers with associated data to yield the source data stream compressed to some ratio, e.g. four-to-one.

Abstract: A dual coating and lift-off method for protecting patterned dielectric-metal coatings using a 2-layer lithography process that is exposed and developed to create an undercut and then, after the wafer is coated with a metal/dielectric filter ending with an incomplete final layer, the top lithography layer is lifted off exposing metal layer edges and leaving the bottom lithography layer intact on the wafer such that the final filter layer(s) can be deposited to complete the coating and passivate the exposed metal layer edges is disclosed.

Abstract: A dual coating and lift-off method for protecting patterned dielectric-metal coatings using a 2-layer lithography process that is exposed and developed to create an undercut and then, after the wafer is coated with a metal/dielectric filter ending with an incomplete final layer, the top lithography layer is lifted off exposing metal layer edges and leaving the bottom lithography layer intact on the wafer such that the final filter layer(s) can be deposited to complete the coating and passivate the exposed metal layer edges is disclosed.