A method of and system of for compressing and decompressing a localized software resource is disclosed. The method may include receiving a software resource, the software resource being in a first language, receiving a localized software resource for compression, where the software resource in the first language is a counterpart of the localized software resource in the second language. Upon receiving the software resources creating a first local dictionary for the localized software resource based at least in part on one or more first language words in the software resource and on data from a global dictionary, and compressing the localized software resource based on the local dictionary.

A distributed network includes a first group of computing devices. Each computing device is to be coupled to two neighbor computing devices of the first group of computing device and is to: (i) aggregate gradient values received from a first neighbor computing device with local gradient values to generate a partial aggregate of gradient values that are to train a neural network model; (ii) transfer the partial aggregate of gradient values to a second neighbor computing device; and repeat (i) and (ii) until a first aggregate of gradient values from the first group of computing devices is buffered at a first computing device of the first group of computing devices. The first computing device is to transfer the first aggregate of gradient values to a second group of computing devices of the distributed network for further aggregation.

Compression techniques by pre-sorting transactions in a consistent way. In at least one embodiment, the compression does not rely on consistent mempool across full nodes. Transactions in a block can be hashed and sorted. Ambiguity can arise from the hashes, which can be resolved using various techniques.

Methods of encoding and decoding are described which use a variable number of instruction words to encode instructions from an instruction set, such that different instructions within the instruction set may be encoded using different numbers of instruction words. To encode an instruction, the bits within the instruction are reordered and formed into instruction words based upon their variance as determined using empirical or simulation data. The bits in the instruction words are compared to corresponding predicted values and some or all of the instruction words that match the predicted values are omitted from the encoded instruction.

A computerized method of compressing symbolic information organized into a plurality of documents, each document having a plurality of symbols, includes: (i) automatically identifying a plurality of sequential and non-sequential symbol pairs in an input document; (ii) counting the number of appearances of each unique symbol pair; and (iii) producing a compressed document that includes a replacement symbol at each position associated with one of the plurality of symbol pairs, at least one of which corresponds to a non-sequential symbol pair. For each non-sequential pair the compressed document includes corresponding indicia indicating a distance between locations of the non-sequential symbols of the pair in the input document. In some instances the plurality of symbol pairs includes only those pairs of non-sequential symbols for which the distance between locations of the non-sequential symbols of the pair in the input document is less than a numeric distance cap.

Embodiments describe a method for speech endpoint detection including receiving identification data for a first state associated with a first frame of speech data from a WFST language model, determining that the first frame of the speech data includes silence data, incrementing a silence counter associated with the first state, copying a value of the silence counter of the first state to a corresponding silence counter field in a second state associated with the first state in an active state list, and determining that the value of the silence counter for the first state is above a silence threshold. The method further includes, determining that an endpoint of the speech has occurred in response to determining that the silence counter is above the silence threshold, and outputting text data representing a plurality of words determined from the speech data that was received prior to the endpoint.

A processor requests that a data transformation operation be performed using another processor, in which the data transformation operation is performed asynchronously. A determination is made that the data transformation operation performed using the other processor has completed unsatisfactorily, and based on the unsatisfactory completion, status relating to performance of the data transformation operation is incomplete. The data transformation operation is then re-executed synchronously using the one processor, and the re-executing provides status information unavailable in performing the data transformation operation asynchronously.

Systems and methods are provided for encoding a multi-pixel caching scheme for lossless encoders. The systems and methods can include obtaining a sequence of pixels, determining repeating sub-sequences of the sequence of pixels consisting of a single repeated pixel and non-repeating sub-sequences of the sequence of pixels, responsive to the determination, encoding the repeating sub-sequences using a run-length of the repeated pixel and encoding the non-repeating sub-sequences using a multi-pixel cache, wherein the encoding using a multi-pixel cache comprises, encoding non-repeating sub-sequences stored in the multi-pixel cache as the location of the non-repeating sub-sequences in the multi-pixel cache, and encoding non-repeating sub-sequences not stored in the multi-pixel cache using the value of the pixels in the non-repeating sub-sequences.

An encoder for encoding weight parameters of a neural network is configured to obtain a plurality of weight parameters of the neural network, to encode the weight parameters of the neural network using a context-dependent arithmetic coding, to select a context for an encoding of a weight parameter, or for an encoding of a syntax element of a number representation of the weight parameter, in dependence on one or more previously encoded weight parameters and/or in dependence on one or more previously encoded syntax elements of a number representation of one or more weight parameters, and to encode the weight parameter, or a syntax element of the weight parameter, using the selected context. Corresponding decoder, quantizer, methods and computer programs are also described.

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.