A communication system SYS includes a transmission apparatus 1 and a reception apparatus 2. The transmission apparatus includes: a conversion unit 111 for converting a bit stream Z having a bit length b into a bit stream Y that has w−1 (w is an integer equal to or larger than 2) bit 1 and that has a bit length n (n>b); a conversion unit 112 for converting the bit stream Y into a bit stream X having a bit length t (t<n); and a Neural Network 113 that has a t input node and that outputs a value relating to a feature of a transmission signal Tx when the bit stream X is inputted thereto. The reception apparatus includes: a Neural Network 212 that has a t output node and that outputs a numerical data stream U including t numerical data when a feature of the reception signal is inputted thereto; a conversion unit 213 for converting the numerical data stream U into a numerical data stream Y′ including n numerical data; and a generation unit 214 for generating a bit stream Z′ having the bit length b by performing, on the numerical data stream U, an inverse conversion of a conversion processing performed by the conversion unit 111.

A weight data compression method includes: generating a 4-bit data string of 4-bit data items each expressed as any one of nine 4-bit values, by dividing ternary weight data into data items each having 4 bits; and generating first compressed data including a first flag value string and a first non-zero value string by (0) generating the first flag value string by assigning one of 0 and 1 as a first flag value of a 1-bit flag to a 4-bit data item 0000 and assigning an other of 0 and 1 as a second flag value of the 1-bit flag to a 4-bit data item other than 0000 among the 4-bit data items in the 4-bit data string and (ii) generating the first non-zero value string by converting the 4-bit data item other than 0000 into a 3-bit data item having any one of eight 3-bit values.

A method of encoding input data includes dividing the input data into a plurality of data packets, an input packet of the plurality of data packets including a plurality of digits in a first base system, base-converting the input packet from the first base system to generate a base-converted packet including a plurality of converted digits in a second base system, the second base system having a base value lower than that of the first base system, and incrementing the converted digits to generate a coded packet for transmission through a communication channel.

A data-sparsity homogenizer includes a plurality of multiplexers and a controller. The plurality of multiplexers receives 2.sup.N bit streams of non-homogenous sparse data in which the non-homogenous sparse data includes non-zero value data clumped together. The plurality of multiplexers is arranged in 2.sup.N rows and N columns. Each input of a multiplexer in a first column receives a respective bit stream of the 2.sup.N bit streams of non-homogenized sparse data, and the multiplexers in a last column output 2.sup.N bit streams of sparse data that is more homogenous than the non-homogenous sparse data of the 2.sup.N bit streams. The controller controls the plurality of multiplexers so that the multiplexers in the last column output the 2.sup.N channels of bit streams of sparse data that is more homogeneous than the non-homogenous sparse data of the 2.sup.N bit streams.

A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. A processing/filtering element utilizes occupancy map information and/or auxiliary patch information to determine relationships between patches in image frames and adjusts encoding/decoding and/or filtering or pre/post-processing parameters based on the determined relationships.

One or more units of decompressed data of a plurality of units of decompressed data is written to a target location for subsequent writing to memory. The plurality of units of decompressed data includes a plurality of symbol outputs and has associated therewith a plurality of decompression headers. A determination is made that the subsequent writing to memory of at least a portion of another unit of decompressed data to be written to the target location is to be stalled. A symbol start position of the other unit of decompressed data and a decompression header of a selected unit of the one or more units of decompressed data written to the target location are provided to a component of the computing environment. The decompression header is used for the subsequent writing of the other unit of decompressed data to memory.

The present embodiments include a system and method for processing multi-dimensional images in real time through the use of third-party servers and cloud computing. The system includes a data acquisition processor, a data storage unit, an administrator processor, and a server. The server can be a cloud-based server. The method includes receiving multi-dimensional imaging data, compressing and blending the image data, transmitting the image data to a server, decompressing and deblending the data, generating multi-dimensional images, and transmitting the imaging data back to the administrator processor.

A method for decoding an encoded audio bitstream is disclosed. The method includes receiving the encoded audio bitstream and decoding the audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analysis filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag.

A deep neural network (“DNN”) module can compress and decompress neuron-generated activation data to reduce the utilization of memory bus bandwidth. The compression unit can receive an uncompressed chunk of data generated by a neuron in the DNN module. The compression unit generates a mask portion and a data portion of a compressed output chunk. The mask portion encodes the presence and location of the zero and non-zero bytes in the uncompressed chunk of data. The data portion stores truncated non-zero bytes from the uncompressed chunk of data. A decompression unit can receive a compressed chunk of data from memory in the DNN processor or memory of an application host. The decompression unit decompresses the compressed chunk of data using the mask portion and the data portion. This can reduce memory bus utilization, allow a DNN module to complete processing operations more quickly, and reduce power consumption.

We disclose herein a method of compressing data for data transfer within an electronic device. The method comprises: receiving, at a first processing member of the electronic device, a plurality of data samples produced by a member of the electronic device, wherein the data samples comprise numerical bits; restructuring, by the first processing member, the plurality of data samples into a plurality of data packets; labelling each data packet with a sample indicator bit to indicate a plurality of groups across the plurality of data packets; transferring a bit stream comprising at least some of the plurality of data packets across an interface of the electronic device to a receiving member of the electronic device; and decoding the bit stream, by a second processing member of the electronic device, to obtain at least some of the plurality of the data samples, the decoding being based at least in part on the sample indicator bits.