A processor-implemented method of performing convolution operations in a neural network includes generating a plurality of first sub-bit groups and a plurality of second sub-bit groups, respectively from at least one pixel value of an input feature map and at least one predetermined weight, performing a convolution operation on a first pair that includes a first sub-bit group including a most significant bit (MSB) of the at least one pixel value and a second sub-bit group including an MSB of the at least one predetermined weight, based on the plurality of second sub-bit groups, obtaining a maximum value of a sum of results for convolution operations of remaining pairs excepting the first pair, and based on a result of the convolution operation on the first pair and the maximum value, determining whether to perform the convolution operations of the remaining pairs.

An in-memory vector addition method for a dynamic random access memory (DRAM) is disclosed which includes consecutively transposing two numbers across a plurality of rows of the DRAM, each number transposed across a fixed number of rows associated with a corresponding number of bits, assigning a scratch-pad including two consecutive bits for each bit of each number being added, two consecutive bits for carry-in (C.sub.in), and two consecutive bits for carry-out-bar (C.sub.out), assigning a plurality of bits in a transposed orientation to hold results as a sum of the two numbers, for each bit position of the two numbers: computing the associated sum of the bit position; and placing the computed sum in the associated bit of the sum.

An in-memory vector addition method for a dynamic random access memory (DRAM) is disclosed which includes consecutively transposing two numbers across a plurality of rows of the DRAM, each number transposed across a fixed number of rows associated with a corresponding number of bits, assigning a scratch-pad including two consecutive bits for each bit of each number being added, two consecutive bits for carry-in (C.sub.in), and two consecutive bits for carry-out-bar (C.sub.out), assigning a plurality of bits in a transposed orientation to hold results as a sum of the two numbers, for each bit position of the two numbers: computing the associated sum of the bit position; and placing the computed sum in the associated bit of the sum.

An apparatus is configured to extract a rightmost bit position of a target value based on input data and a complement of the input data, sequentially extract a bit position of the target value, and output the extracted bit position.

An arithmetic processing device includes one or more memories; and one or more processors includes execute an operation of fixed-point number data, acquire statistical information that indicates a distribution of positions of most significant bits of a plurality of fixed-point number data obtained by the operation, update, based on the statistical information, a range for restriction of bit width of the plurality of fixed-point number data to be used for the operation, estimate respective data amount after compression of the plurality of fixed-point number data by a plurality of compression methods based on the statistical information, determine a compression method by which data amount after compression of the plurality of fixed-point number data is minimum among plurality of compression methods, transfer the plurality of fixed-point number data compressed by the compression method to the one or more memories.

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.

Methods and leading zero anticipators for estimating the number of leading zeros in a result of a fixed point arithmetic operation which is accurate to within one bit for any signed fixed point numbers. The leading zero anticipator includes an input encoding circuit which generates an encoded input string from the fixed point numbers; a window-based surrogate string generation circuit which generates a surrogate string whose leading one is an estimate of the leading one in the result of the arithmetic operation by examining consecutive windows of the encoded input string and setting corresponding bits of the surrogate string based on the examinations; and a counter circuit configured to estimate the number of leading zeros in the result of the arithmetic operation based on the leading one in the surrogate string.

Methods and leading zero anticipators for estimating the number of leading zeros in a result of a fixed point arithmetic operation which is accurate to within one bit for any signed fixed point numbers. The leading zero anticipator includes an input encoding circuit which generates an encoded input string from the fixed point numbers; a window-based surrogate string generation circuit which generates a surrogate string whose leading one is an estimate of the leading one in the result of the arithmetic operation by examining consecutive windows of the encoded input string and setting corresponding bits of the surrogate string based on the examinations; and a counter circuit configured to estimate the number of leading zeros in the result of the arithmetic operation based on the leading one in the surrogate string.

An in-memory vector addition method for a dynamic random access memory (DRAM) is disclosed which includes consecutively transposing two numbers across a plurality of rows of the DRAM, each number transposed across a fixed number of rows associated with a corresponding number of bits, assigning a scratch-pad including two consecutive bits for each bit of each number being added, two consecutive bits for carry-in (C.sub.in), and two consecutive bits for carry-out-bar (C.sub.out), assigning a plurality of bits in a transposed orientation to hold results as a sum of the two numbers, for each bit position of the two numbers: computing the associated sum of the bit position; and placing the computed sum in the associated bit of the sum.

An in-memory vector addition method for a dynamic random access memory (DRAM) is disclosed which includes consecutively transposing two numbers across a plurality of rows of the DRAM, each number transposed across a fixed number of rows associated with a corresponding number of bits, assigning a scratch-pad including two consecutive bits for each bit of each number being added, two consecutive bits for carry-in (C.sub.in), and two consecutive bits for carry-out-bar (C.sub.out), assigning a plurality of bits in a transposed orientation to hold results as a sum of the two numbers, for each bit position of the two numbers: computing the associated sum of the bit position; and placing the computed sum in the associated bit of the sum.