113 results about "Simd processing" patented technology

A method, computer program product, and information handling system for generating mixed-mode operations in the compilation of program code for processors having vector or SIMD processing units is disclosed. In a preferred embodiment of the present invention, program instructions making up the body of a loop are abstracted into virtual vector instructions. These virtual vector instructions are treated, for initial code optimization purposes, as vector instructions (i.e., instructions written for the vector unit). The virtual vector instructions are eventually expanded into native code for the target processor, at which time a determination is made for each virtual vector instruction as to whether to expand the virtual vector instruction into native vector instructions, into native scalar instructions, into calls to pre-defined library functions, or into a combination of these. A cost model is used to determine the optimal choice of expansion based on hardware/software constraints, performance costs/benefits, and other criteria.

The present invention provides an efficient method to implement nested if-then-else conditional statements in a SIMD processor, which requires only one vector compare instruction for both if and else parts of the conditional construct. No stack and stack-handling instructions are needed for vector condition codes. Two condition code flag bits representing if and else parts of testing per element provide for nesting of multiple if-then-else. All SIMD instructions are conditional including the vector compare instruction, and this provides a method for aggregating multiple conditions in nested if-then-else statements. M full levels of if-then-else nesting requires (2^M−1) nodes or vector test instructions and 2^M+1 condition code flags per vector element. Also, capability to compare any element of first source vector register with any element of second source vector is provided.

The present invention provides alignment and ordering of vector elements for SIMD processing. In the alignment of vector elements for SIMD processing, one vector is loaded from a memory unit into a first register and another vector is loaded from the memory unit into a second register. The first vector contains a first byte of an aligned vector to be generated. Then, a starting byte specifying the first byte of an aligned vector is determined. Next, a vector is extracted from the first register and the second register beginning from the first bit in the first byte of the first register continuing through the bits in the second register. Finally, the extracted vector is replicated into a third register such that the third register contains a plurality of elements aligned for SIMD processing. In the ordering of vector elements for SIMD processing, a first vector is loaded from a memory unit into a first register and a second vector is loaded from the memory unit into a second register. Then, a subset of elements are selected from the first register and the second register. The elements from the subset are then replicated into the elements in the third register in a particular order suitable for subsequent SIMD vector processing.

Methods and apparatus provide for disabling at least some data path processing circuits of a SIMD processing pipeline, in which the processing circuits are organized into a matrix of slices and stages, in response to one or more enable flags during a given cycle.

This invention deals with the problem of paralleling random read access within a reasonably sized block of data for a vector SIMD processor. The invention sets up plural parallel look up tables, moves data from main memory to each plural parallel look up table and then employs a look up table read instruction to simultaneously move data from each parallel look up table to a corresponding part a vector destination register. This enables data processing by vector single instruction multiple data (SIMD) operations. This vector destination register load can be repeated if the tables store more used data. New data can be loaded into the original tables if appropriate. A level one memory is preferably partitioned as part data cache and part directly addressable memory. The look up table memory is stored in the directly addressable memory.

A data processing apparatus (2) comprising: a register data store operable to store data elements; an instruction decoder (14, 16) operable to decode an instruction with generated constant, said instruction having a data value associated therewith; a data processor. (18) operable to perform data processing operations within parallel processing lanes on at least one source operand in response to a data processing instruction decoded by said instruction decoder (16); and said data processor being operable in response to said decoded instruction with generated constant and associated data value to expand at least a data portion (1210) of said associated data value, said expansion being performed in response to said instruction with generated constant and depending on a selected function, to generate a constant (1240), said generated constant (1240) forming one of said at least one source operands.

The present invention provides a 16×16-sliding window using vector register file with zero overhead for horizontal or vertical shifts to incorporate motion estimation into SIMD vector processor architecture. SIMD processor's vector load mechanism, vector register file with shifting of elements capability, and 16×16 parallel SAD calculation hardware and instruction are used. Vertical shifts of all sixteen-vector registers occur in a ripple-through fashion when the end vector register is loaded. The parallel SAD calculation hardware can calculate one 16-by-16-block match per clock cycle in a pipelined fashion. In addition, hardware for best-match SAD value comparisons and maintaining their pixel location reduces the software overhead. Block matching for less than 16 by 16 block areas is supported using a mask register to mask selected elements, thereby reducing search area to any block size less than 16 by 16.

The present invention provides efficient ways to implement scan conversion and matrix transpose operations using vector multiplex operations in a SIMD processor. The present method provides a very fast and flexible way to implement different scan conversions, such as zigzag conversion, and matrix transpose for 2×2, 4×4, 8×8 blocks commonly used by all video compression and decompression algorithms.

This invention provides a system and method for processing discrete image data within an overall set of acquired image data based upon a focus of attention within that image. The result of such processing is to operate upon a more limited subset of the overall image data to generate output values required by the vision system process. Such output value can be a decoded ID or other alphanumeric data. The system and method is performed in a vision system having two processor groups, along with a data memory that is smaller in capacity than the amount of image data to be read out from the sensor array. The first processor group is a plurality of SIMD processors and at least one general purpose processor, co-located on the same die with the data memory. A data reduction function operates within the same clock cycle as data-readout from the sensor to generate a reduced data set that is stored in the on-die data memory. At least a portion of the overall, unreduced image data is concurrently (in the same clock cycle) transferred to the second processor while the first processor transmits at least one region indicator with respect to the reduced data set to the second processor. The region indicator represents at least one focus of attention for the second processor to operate upon.

An apparatus and method are provided for performing rearrangement operations and arithmetic operations on data. The data processing apparatus has processing circuitry for performing SIMD processing operations and scalar processing operations, a register bank for storing data and control circuitry responsive to program instructions to control the processing circuitry to perform data processing operations. The control circuitry is arranged to responsive to a combined rearrangement arithmetic instruction to control the processing circuitry to perform a rearrangement operation and at least one SIMD arithmetic operation on a plurality of data elements stored in the register bank. The rearrangement operation is configurable by a size parameter derived at least in part from the register bank. The size parameter provides an indication of a number of data elements forming a rearrangement element for the purposes of the rearrangement operation. The associated method involves controlling processing circuitry to perform a rearrangement operation and at least one SIMD arithmetic operation in response to a combined rearrangement arithmetic instruction and providing the scalar logic size parameter to configure the rearrangement operation. Computer program product is also provided comprising at least one combined rearrangement arithmetic instruction.

Execution of a single stand-alone instruction manipulates two n bit strings of data to pack data or align the data. Decoding of the single instruction identifies two registers of n bits each and a shift value, preferably as parameters of the instruction. A first and a second subset of data of less than n bits are selected, by logical shifting, from the two registers, respectively, based solely upon the shift value. Then, the subsets are concatenated, preferably by a logical OR, to obtain an output of n bits. The output may be aligned data or packed data, particularly useful for performing a single operation on multiple sets of the data through parallel processing with a SIMD processor.

A scalar processor that includes a plurality of scalar arithmetic logic units and a special function unit. Each scalar unit performs, in a different time interval, the same operation on a different data item, where each different time interval is one of a plurality of successive, adjacent time intervals. Each unit provides an output data item in the time interval in which the unit performs the operation and provides a processed data item in the last of the successive, adjacent time intervals. The special function unit provides a special function computation for the output data item of a selected one of the scalar units, in the time interval in which the selected scalar unit performs the operation, so as to avoid a conflict in use among the scalar units. A vector processing unit includes an input data buffer, the scalar processor, and an output orthogonal converter.

The present invention relates to an efficient implementation of color space conversion in a SIMD processor as part of converting output of video decompression to interface to a display unit.

A SIMD processor responds to a single min/max instruction to find the minimum or maximum valued data unit in an array of data units. The determined minimum/maximum value and an associated index value thereto may be output. Alternatively, the value of a data unit in another array may be output at a corresponding location. A further single instruction executable by the SIMD processor, may be applied to results obtained using such a single min/max instruction, to allow such instructions to operate on two dimensional arrays.

In view of a necessity of alleviating factors obstructing an effect of SIMD operation such as in-register data alignment in high speed formation of an SIMD processor, numerous data can be supplied to a data alignment operation pipe 211 by dividing a register file into four banks and enabling to designate a plurality of registers by a single piece of operand to thereby enable to make access to four registers simultaneously and data alignment operation can be carried out at high speed. Further, by defining new data pack instruction, data unpack instruction and data permutation instruction, data supplied in a large number can be aligned efficiently. Further, by the above-described characteristic, definition of multiply accumulate operation instruction maximizing parallelism of SIMD can be carried out.