Software instructions are executed on a processor within a computer system to configure a steaming engine with stream parameters to define a multidimensional array. The stream parameters define a size for each dimension of the multidimensional array and a pad value indicator. Data is fetched from a memory coupled to the streaming engine responsive to the stream parameters. A stream of vectors is formed for the multidimensional array responsive to the stream parameters from the data fetched from memory. A padded stream vector is formed that includes a specified pad value without accessing the pad value from system memory.

A layout pattern of a two-port ternary content addressable memory (TCAM) includes a first storage unit, a second storage unit, a first comparison circuit and a second comparison circuit. The first comparison circuit and the second comparison circuit are positioned in a first side area of a side and a second side area of another side of the layout pattern, respectively. The first storage unit and the second storage unit are positioned in a first middle area and a second middle area between the first side area and the second side area, respectively. The first storage unit is connected to the first comparison circuit through a first gate structure and connected to the second comparison circuit through a second gate structure. The second storage unit is connected to the first comparison circuit through a third gate structure and connected to the second comparison circuit through a fourth gate structure.

An electronic device includes a coding module that determines whether a parameter of an artificial neural network is an outlier, depending on a value of the parameter and compresses the parameter by truncating a first bit of the parameter when the parameter is a non-outlier and truncating a second bit of the parameter when the parameter is the outlier, and a decoding module that decodes a compressed parameter.

An electronic device includes a coding module that determines whether a parameter of an artificial neural network is an outlier, depending on a value of the parameter and compresses the parameter by truncating a first bit of the parameter when the parameter is a non-outlier and truncating a second bit of the parameter when the parameter is the outlier, and a decoding module that decodes a compressed parameter.

Verifying the correctness of a leading zero counter, including: generating, based on an input value comprising a plurality of digits, a first bit vector, wherein each entry of the first bit vector indicates whether a corresponding digit of the input value is equal to zero; calculating, based on the first bit vector, a leading zero count for the input value; generating a bit mask comprising a number of leading ones equal to the leading zero count; generating a second bit vector comprising a one at a same index as a first occurring zero in the bit mask; and verifying the leading zero count based on the first bit vector and one or more of the bit mask and the second bit vector.

Verifying the correctness of a leading zero counter, including: generating, based on an input value comprising a plurality of digits, a first bit vector, wherein each entry of the first bit vector indicates whether a corresponding digit of the input value is equal to zero; calculating, based on the first bit vector, a leading zero count for the input value; generating a bit mask comprising a number of leading ones equal to the leading zero count; generating a second bit vector comprising a one at a same index as a first occurring zero in the bit mask; and verifying the leading zero count based on the first bit vector and one or more of the bit mask and the second bit vector.

Rounding hexadecimal floating point numbers using binary incrementors, including: incrementing, by a first incrementor, a first subset of bits of an operand comprising a binary hexadecimal floating point operand; incrementing, by a second incrementor, a second subset of bits of the operand; generate an intermediate result based on a carryout of the second incrementor; and generate an incremented result based on a carryout of the first incrementor and one or more of: a first bit of the intermediate result or the carryout of the second incrementor.

A method for finding the first one bit in a vector groups the bits of the vector into pairs of adjacent bits. Within each pair of two the presence or absence of a one bit is identified and the position determined. There may be multiple levels so a second level groups has inputs based on the determinations from the two pairs. Based on the inputs the presence of absence of a one bit is determined and, if present, the position within the respective bits of the vector. This can be repeated over multiple levels.

A method for finding the first one bit in a vector groups the bits of the vector into pairs of adjacent bits. Within each pair of two the presence or absence of a one bit is identified and the position determined. There may be multiple levels so a second level groups has inputs based on the determinations from the two pairs. Based on the inputs the presence of absence of a one bit is determined and, if present, the position within the respective bits of the vector. This can be repeated over multiple levels.

A data processing platform, method, and program product perform compression and decompression of a set of data items. Suffix data and a prefix are selected for each respective data item in the set of data items based on data content of the respective data item. The set of data items is sorted based on the prefixes. The prefixes are encoded by querying multiple encoding tables to create a code word containing compressed information representing values of all prefixes for the set of data items. The code word and suffix data for each of the data items are stored in memory. The code word is decompressed to recover the prefixes. The recovered prefixes are paired with their respective suffix data.