According to some embodiments of the present disclosure, a device is disclosed. In embodiments, the device stores a computer program comprised of a set of encoded executable instructions; a genomic differentiation object and genomic regulation instructions (GRI) that were used to encode the set of encoded executable instructions. The device further includes a processing system comprising a VDAX and a set of processing cores. The VDAX is configured to: receive encoded instructions to be executed from the set of encoded executable instructions and decode the encoded instructions into decoded executable instruction based on a modified genomic differentiation object and sequences extracted from metadata associated with the encoded instructions. In these embodiments, the modified genomic differentiation object is modified from the genomic differentiation object using the GRI. The set of processing cores are configured to receive the decoded executable instructions from the VDAX and to execute the decoded executable instructions.

A method for executing computer programs in a trusted execution environment of a device is disclosed. The method includes retrieving a genomic differentiation object corresponding to a computer program that comprises a set of encoded executable instructions. The method further includes modifying the genomic differentiation object based on genomic regulation instructions (GRI) to obtain a modified genomic differentiation object, wherein the GRI were used to encode the set of encoded executable instructions of the computer program. The method includes obtaining a first instruction that is to be executed from the first set of encoded executable instructions of the computer program; obtaining a first sequence from first metadata associated with the first encoded instruction; generating a genomic engagement factor (GEF) based on the first sequence and the modified genomic differentiation object; decoding the first encoded instruction using the GEF to obtain a first decoded instruction; and executing the first decoded instruction.

A method for maintaining a material data blockchain (MDC) is disclosed. The method includes receiving a material data block (MDB), wherein the MDB includes a metadata portion and a payload portion. The method further includes extracting a first sequence from the metadata portion and generating a genomic engagement factor (GEF) based on the sequence, a genomic differentiation object assigned to the creator VDAX, and genomic regulation instructions (GRI) that are maintained by the creator VDAX. The method further includes generating a creator value corresponding to the MDB based on the first GEF and the MDB and digitally signing the MDB with the creator value. The method includes providing the unnotarized MDB to one or more notary cohorts; and receiving a respective notary value from each of the notary cohorts, wherein each notary value is generated using respective GRI and genomic differentiation object maintained by a respective notary.

A method for verifying a material data chain (MDC) that is maintained by a creator is disclosed. The method includes receiving an unverified portion of the MDC from the creator including a set of consecutive material data blocks (MDBs). Each respective MDB includes respective material data, respective metadata, and a creator verification value. The method includes modifying a genomic differentiation object assigned to the verification cohort based on first genomic regulation instructions (GRI) that were used by the creator to generate the creator verification value. For each MDB in the unverified portion, the method includes determining a verifier verification value based on the MDB, a preceding MDB in the MDC, and a genomic engagement factor (GEF) determined with respect to the MDB. The GEF corresponding to an MDB is determined by extracting a sequence from the metadata of a MDB and mapping the sequence into the modified genomic differentiation object.

A method and apparatus for encoding and decoding of floating-point number is provided. The method for encoding is used to convert at least one original floating-point number to at least one encoded floating-point number. The method for encoding includes: determining a number of exponent bits of the at least one encoded floating-point number and calculating an exponent bias according to at least one original exponent value of the at least one original floating-point number; and converting an original exponent value of a current original floating-point number of the at least one original floating-point number to an encoded exponent value of a current encoded floating-point number of the at least one encoded floating-point number according to the exponent bias.

Techniques are disclosed for identifying fixed bits of a bitstring format. One or more processors are configured to generate a first bitstring having respective first bit values that have a first satisfiability state and generate a second bitstring having respective second bit values that have a second satisfiability state. The one or more processors are configured to identify first potential free bits having respective first common values and generate a third bitstring having first potential free bits with the respective first common values and third remaining bits. The one or more processors are configured to identify second potential free bits having respective second common values and identify a fixed bit that is not included in the first potential free bits and is not included in the second potential free bits.

A computer-implemented method for improving compression of predictive models includes generating an unlabeled simulated data set by expanding an initial data set, and generating a labeled data set by predicting the unlabeled, simulated data set using a complex model to output a plurality of labels. The method also includes training a relatively simple neural network using the labeled data set.

A memory includes, in one embodiment, one or more storage elements; read/write circuitry; and compressed bit circuitry. The read/write circuitry is configured to read a set of hard bits from the one or more storage elements, and sense a set of soft bits while reading the set of hard bits from the one or more storage elements, the set of soft bits having a first fixed size, and the set of soft bits indicating a reliability of the set of hard bits. The compressed soft bit circuitry is configured to generate, with a fixed size soft bit lossy compression algorithm, a fixed size compressed soft bits by compressing the set of soft bits, the fixed size compressed soft bits having a second fixed size that is smaller than the first fixed size, and output the fixed size compressed soft bits to a memory-to-controller bus.

A method for executing computer programs in a trusted execution environment of a device is disclosed. The method includes retrieving a genomic differentiation object corresponding a computer program; modifying the genomic differentiation object based on genomic regulation instructions (GRI) to obtain a modified genomic differentiation object; and executing a first executable instruction of the computer program. Executing the first executable instruction includes: retrieving first encoded data that is input to the first executable instruction; extracting a sequence from metadata associated with the encoded data; generating a first genomic engagement factor (GEF) based on the first sequence, the GRI and, and the modified genomic differentiation object; decoding the first encoded data based on the first GEF to obtain first decoded data; and executing the first executable instruction using the first decoded data.

A method is presented for compressing data of a Rectified Linear Unit (ReLU) function on a graphical processing unit (GPU) employed in a learning process of a deep neural network. The method includes converting an initial data structure including nonzero data and zero data into a compressed data structure including only the nonzero data of the initial data structure as compressed data by generating a nonzero data bitmap region, generating a nonzero data number table region by employing a parallel reduction algorithm, calculating a nonzero data array index per block region of all blocks from the nonzero data number table region by employing a parallel prefix sum scan algorithm, allocating a buffer for the compressed data; and copying the nonzero data from the initial data structure into a nonzero data array region in a compressed data format in parallel.