A method for blockchain-based transaction consensus processing is provided. Node devices in a blockchain include at least one primary node device and several secondary node devices, the primary node device fragments proposed transaction data into a specified number of data fragments based on an erasure code algorithm, and the method includes: receiving a data fragment of the transaction data that is sent by the primary node device in a unicast mode, where respective data fragments sent by the primary node device to individual node devices in a unicast mode are different from one another; broadcasting the received data fragment to other node devices in the blockchain, and receiving data fragments of the transaction data that are broadcast by the other node devices; determining whether the number of received data fragments of the transaction data reaches an erasure code recovery threshold; and if so, performing data recovery on the received data fragments based on an erasure code reconstruction algorithm to obtain original content of the transaction data, to complete consensus processing with respect to the original content of the transaction data.

Disclosed is an operating method of a semiconductor device, including acquiring resource information on a plurality of hardware resources, receiving a compression request or a decompression request for data, acquiring context information on the semiconductor device, in response to receiving the compression request or the decompression request for the data, selecting a compression algorithm for compressing or decompressing the data, based on the context information, selecting, among the plurality of hardware resources, a hardware resource for performing the selected compression algorithm, based on the acquired resource information, and compressing or decompressing the data using the selected compression algorithm and the selected hardware resource.

A storage system receives one or more records from a host system. The records are compressed in a first compression format that is native to the host system. The storage system identifies an incompatibility between the first compression format and a first operation of the storage system. In response to the identified incompatibility, the storage system decompresses the received records. The decompression is based on the first compression format. The storage system compresses the decompressed records in a second compression format. The storage system stores the secondarily compressed records onto a storage medium.

Technologies for providing accelerated functions as a service in a disaggregated architecture include a compute device that is to receive a request for an accelerated task. The task is associated with a kernel usable by an accelerator sled communicatively coupled to the compute device to execute the task. The compute device is further to determine, in response to the request and with a database indicative of kernels and associated accelerator sleds, an accelerator sled that includes an accelerator device configured with the kernel associated with the request. Additionally, the compute device is to assign the task to the determined accelerator sled for execution. Other embodiments are also described and claimed.

A compute device to manage workflow to disaggregated computing resources is provided. The compute device comprises a compute engine receive a workload processing request, the workload processing request defined by at least one request parameter, determine at least one accelerator device capable of processing a workload in accordance with the at least one request parameter, transmit a workload to the at least one accelerator device, receive a work product produced by the at least one accelerator device from the workload, and provide the work product to an application.

[Object] To achieve both continuity of the system running and reduction of the running cost under a situation in which a storage region on a network is used as a saving destination of various kinds of data. [Solution] Provided is a an information processing device including: a signal processing unit that encodes a first signal including one or more non-zero components based on first data and one or more zero components into a second signal having a shorter signal length than a signal length of the first signal on the basis of a matrix generated in accordance with a predetermined condition; a data generation unit that generates one or more pieces of second data by associating information indicating positions of signal elements in the second signal with the signal elements in the second signal; and a transmission unit that transmits each of the one or more generated pieces of second data to one or more devices connected via a network.

A method, computer program, and computer system is provided for compressing a deep neural network model. Weight coefficients associated with a deep neural network are quantize and entropy-coded. The quantized and entropy-coded weight coefficients are locally smoothed. The smoothed weight coefficients are compressed based on applying a variational dropout to the weight coefficients.

A system and method to encrypt a block of data is disclosed. A block of original data is retrieved from a data store, block of original data including a N number of words, each word including one or more bits of data. A multiplier matrix is provided, the multiplier matrix having NN words, a plurality of sub matrices arranged diagonally within the NN matrix, with each of the sub matrix arranged as a binomial matrix. All the words in the multiplier matrix not part of the sub matrix are set to zero. The block of original data is multiplied with the multiplier matrix to generate a block of modified original data with N number of words.

Technologies for offloading acceleration task scheduling operations to accelerator sleds include a compute device to receive a request from a compute sled to accelerate the execution of a job, which includes a set of tasks. The compute device is also to analyze the request to generate metadata indicative of the tasks within the job, a type of acceleration associated with each task, and a data dependency between the tasks. Additionally the compute device is to send an availability request, including the metadata, to one or more micro-orchestrators of one or more accelerator sleds communicatively coupled to the compute device. The compute device is further to receive availability data from the one or more micro-orchestrators, indicative of which of the tasks the micro-orchestrator has accepted for acceleration on the associated accelerator sled. Additionally, the compute device is to assign the tasks to the one or more micro-orchestrators as a function of the availability data.

An example method of compressing a data set includes determining whether individual values from a data set correspond to a first category or a second category of values. Based on one of the values corresponding to the first category, the value is added to a compressed data set. Based on one of the values corresponding to the second category, the value is excluded from the compressed data set, and a statistical distribution of values of the second category is updated based on the value. During a first phase, the determining is performed for a plurality of values from a first portion of the data set based on comparison of the values to criteria. During a second phase, the determining is performed for a plurality of values from a second portion of the data set based on the statistical distribution.