Embodiments of the present disclosure disclose a method and apparatus for compressing a deep learning model. An embodiment of the method includes: acquiring a to-be-compressed deep learning model; pruning each layer of weights of the to-be-compressed deep learning model in units of channels to obtain a compressed deep learning model; and sending the compressed deep learning model to a terminal device, so that the terminal device stores the compressed deep learning model. By pruning each layer of weights of the deep learning model in units of channels, the parameter redundancy of the deep learning model is effectively reduced, thereby improving the computational speed of the deep learning model and maintaining the model accuracy.

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 code decompression engine reads compressed code from a memory containing a series of code parts and a dictionary part. The code parts each have a bit indicating compressed or uncompressed. When the code part is compressed, it has a value indicating the number of segments, followed by the segments, followed by an index into the dictionary part. The decompressed instruction is the dictionary value specified by the index, which is modified by the segments. Each segment describes the modification to the dictionary part specified by the index by a mask type, a mask offset, and a mask.

An information handling system improves removal of steganography data embedded in a graphics file by processing graphics files stored in a file system or transmitted through a network by processing the graphics files in a steganalyzer. The steganalyzer converts the body segment of the graphics file into binary code, and then compresses the binary code into a graphics file. This process results in the removal of any potential malicious code. The body segment location can be determined by parsing the portable network graphics file to determine a location of a pre-fix graphics file signature and a post-fix graphics file signature, with the graphics files signatures being specific to a particular type of graphics file.

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.

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.

An application activation method is provided. The method includes obtaining a first compressed file, where the first compressed file contains activation information of an application and compressed content of a code package of the application. The method also includes extracting the compressed content from the first compressed file; generating a second compressed file by using the compressed content without decompressing the compressed content; and loading the second compressed file, and activating the application according to the activation information in the first compressed file.

A computing system and a compressing method for neural network parameters are provided. In the method, multiple neural network parameters are obtained. The neural network parameters are used for a neural network algorithm. Every at least two neural network parameters are grouped into an encoding combination. The number of neural network parameters in each encoding combination is the same. The encoding combinations are compressed with the same compression target bit number. Each encoding combination is compressed independently. The compression target bit number is not larger than a bit number of each encoding combination. Thereby, the storage space can be saved and excessive power consumption for accessing the parameters can be prevented.

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 re-ordered 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.

Described herein is a neural network accelerator (NNA) with a decompression unit that can be configured to perform multiple types of decompression. The decompression may include a separate subunit for each decompression type. The subunits can be coupled to form a pipeline in which partially decompressed results generated by one subunit are input for further decompression by another subunit. Depending on which types of compression were applied to incoming data, any number of the subunits may be used to produce a decompressed output. In some embodiments, the decompression unit is configured to decompress data that has been compressed using a zero value compression scheme, a shared value compression scheme, or both. The NNA can also include a compression unit implemented in a manner similar to that of the decompression unit.