Systems, apparatus, and methods for thread-based scheduling within a multicore processor. Neural networking uses a network of connected nodes (aka neurons) to loosely model the neuro-biological functionality found in the human brain. Various embodiments of the present disclosure use thread dependency graphs analysis to decouple scheduling across many distributed cores. Rather than using thread dependency graphs to generate a sequential ordering for a centralized scheduler, the individual thread dependencies define a count value for each thread at compile-time. Threads and their thread dependency count are distributed to each core at run-time. Thereafter, each core can dynamically determine which threads to execute based on fulfilled thread dependencies without requiring a centralized scheduler.

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.

Embodiments of the present disclosure include a digital circuit and method for compressing input digital values. A plurality of input digital values may include zero values and non-zero values. The input digital values are received on M inputs of a first switching stage. The first switching stage is arranged in groups that rearrange the non-zero values on first switching stage outputs according to a compression and shift. The compression and shift position the non-zero values on outputs coupled to inputs of a second switching stage. The second switching stage consecutively couples non-zero values to N outputs, where N is less than M.

Generally, the present disclosure is directed to systems and methods of quantizing a database with respect to a novel loss or quantization error function which applies a weight to an error measurement of quantized elements respectively corresponding to the datapoints in the database. The weight is determined based on the magnitude of an inner product between the respective datapoints and a query compared therewith. In contrast to previous work, embodiments of the proposed loss function are responsive to the expected magnitude of an inner product between the respective datapoints and a query compared therewith and can prioritize error reduction for higher-ranked pairings of the query and the datapoints. Thus, the systems and methods of the present disclosure provide solutions to some of the problems with traditional quantization approaches, which regard all error as equally impactful.

Systems, apparatus, and methods for thread-based scheduling within a multicore processor. Neural networking uses a network of connected nodes (aka neurons) to loosely model the neuro-biological functionality found in the human brain. Various embodiments of the present disclosure use thread dependency graphs analysis to decouple scheduling across many distributed cores. Rather than using thread dependency graphs to generate a sequential ordering for a centralized scheduler, the individual thread dependencies define a count value for each thread at compile-time. Threads and their thread dependency count are distributed to each core at run-time. Thereafter, each core can dynamically determine which threads to execute based on fulfilled thread dependencies without requiring a centralized scheduler.

A method for partitioning of input vectors for coding is presented. The method comprises obtaining of an input vector. The input vector is segmented, in a non-recursive manner, into an integer number, N.sup.SEG, of input vector segments. A representation of a respective relative energy difference between parts of the input vector on each side of each boundary between the input vector segments is determined, in a recursive manner. The input vector segments and the representations of the relative energy differences are provided for individual coding. Partitioning units and computer programs for partitioning of input vectors for coding, as well as positional encoders, are presented.

A non-transitory computer-readable recording medium having stored therein an update program that causes a computer to execute a procedure, the procedure includes calculating a selection rate of each of a plurality of quantization points included in a quantization table, based on quantization data obtained by quantizing features of a plurality of utterance data, and updating the quantization table by updating the plurality of quantization points based on the selection rate.

For example, a processor may be configured to generate compressed radar information by compressing radar values in a plurality of data bins of at least one radar processing dimension, the at least one radar processing dimension including a range dimension. For example, the processor may be configured to generate the compressed radar information by quantizing a plurality of normalized values corresponding to the radar values in the plurality of data bins. For example, a normalized value corresponding to a radar value in a data bin may be based on a normalization of the radar value with respect to a plurality of radar values in the data bin. For example, the processor may be configured to store the compressed radar information in a memory.

Methods and apparatus for compressing data streams. In an embodiment, a method includes calculating a probability distribution function (PDF) for scaler data, matching the PDF to PDF templates to determine a closest matching PDF template, and selecting an encoder corresponding to the closest matching PDF template wherein a corresponding encoder identifier is determined. The method also includes encoding the scaler data with the encoder to generate an encoded stream, and transmitting the encoded stream and the encoder identifier.

Methods, systems, articles of manufacture, and apparatus are disclosed to decode zero-value-compression data vectors. An example apparatus includes: a buffer monitor to monitor a buffer for a header including a value indicative of compressed data; a data controller to, when the buffer includes compressed data, determine a first value of a sparse select signal based on (1) a select signal and (2) a first position in a sparsity bitmap, the first value of the sparse select signal corresponding to a processing element that is to process a portion of the compressed data; and a write controller to, when the buffer includes compressed data, determine a second value of a write enable signal based on (1) the select signal and (2) a second position in the sparsity bitmap, the second value of the write enable signal corresponding to the processing element that is to process the portion of the compressed data.