Abstract:
A processor includes an instruction schedule and dispatch (schedule/dispatch) unit to receive a single instruction multiple data (SIMD) instruction to perform an operation on multiple data elements stored in a storage location indicated by a first source operand. The instruction schedule/dispatch unit is to determine a first of the data elements that will not be operated to generate a result written to a destination operand based on a second source operand. The processor further includes multiple processing elements coupled to the instruction schedule/dispatch unit to process the data elements of the SIMD instruction in a vector manner, and a power management unit coupled to the instruction schedule/dispatch unit to reduce power consumption of a first of the processing elements configured to process the first data element.
Abstract:
An apparatus and method for complex matrix multiplication. For example, one embodiment of a processor comprises: a decoder to decode a first complex matrix multiplication instruction; execution circuitry to execute the first complex matrix multiplication instruction, the execution circuitry comprising parallel multiplication circuitry to multiply real values from the first plurality of real and imaginary values with corresponding real values from the second plurality of real and imaginary values to generate a first plurality of real products, to multiply imaginary values from the first plurality of real and imaginary values with corresponding imaginary values from the second plurality of real and imaginary values to generate a second plurality of real products; and addition/subtraction circuitry to subtract each real product in the second plurality of real products from a corresponding real product in the first plurality of real products to produce a corresponding real value in the result matrix. The decoder may also decode and the execution circuitry may execute a second complex matrix multiplication instruction to multiply real and imaginary values from the first plurality with corresponding imaginary and real values, respectively, from the second plurality to generate first and second pluralities of imaginary products, and to add corresponding imaginary products to produce a corresponding imaginary value in the result matrix.
Abstract:
Systems, methods, and apparatuses relating to interleaving data values. An embodiment includes decoding circuitry to decode a single instruction, the instruction having one or more fields to specify an opcode, one or more fields to specify a location of a first source operand, one or more fields to specify a location of a second source operand, one or more fields to specify a location of a destination operand, and one or more fields to specify an index value to be used to index a row in the first source operand, wherein the opcode is to indicate execution circuitry is to downconvert data elements of the indexed row of the first source operand, interleave the downconverted elements with data elements of the second source operand, and store the interleaved elements in the destination operand; and execution circuitry to execute the decoded instruction according to the opcode.
Abstract:
Embodiments for a matrix transpose and multiply operation are disclosed. In an embodiment, a processor includes a decoder and execution circuitry. The decoder is to decode an instruction having a format including an opcode field to specify an opcode, a first destination operand field to specify a destination matrix location, a first source operand field to specify a first source matrix location, and a second source operand field to specify a second source matrix location. The execution circuitry is to, in response to the decoded instruction, transpose the first source matrix to generate a transposed first source matrix, perform a matrix multiplication using the transposed first source matrix and the second source matrix to generate a result, and store the result in a destination matrix location.
Abstract:
Techniques for performing square root or reciprocal square root calculations on FP8 data elements in response to an instruction are described. An example of an instruction is one that includes fields for an opcode, an identification of a location of a packed data source operand, and an identification of a packed data destination operand, wherein the opcode is to indicate that execution circuitry is to perform, for each data element position of the packed data source operand, a calculation of a square root value of a FP8 data element in that position and store a result of each square root into a corresponding data element position of the packed data destination operand.
Abstract:
Techniques for converting FP16 or FP32 data elements to FP8 data elements using a single instruction are described. An exemplary apparatus includes decoder circuitry to decode a single instruction, the single instruction to include a one or more fields to identify a source operand, one or more fields to identify a destination operand, and one or more fields for an opcode, the opcode to indicate that execution circuitry is to convert packed half-precision floating-point data or single-precision floating point data from the identified source to packed FP8 data and store the packed bfloat8 data into corresponding data element positions of the identified destination operand; and execution circuitry to execute the decoded instruction according to the opcode to convert packed half-precision floating-point data or single-precision floating point data from the identified source to packed bfloat8 data and store the packed bfloat8 data into corresponding data element positions.
Abstract:
Techniques for performing arithmetic operations on FP8 values are described. An exemplary instruction includes fields for an opcode, an identification of a location of a first packed data source operand, an identification of a location of a second packed data source operand, and an identification of location of a packed data destination operand, wherein the opcode is to indicate an arithmetic operation execution circuitry is to perform, for each data element position of the identified packed data source operands, the arithmetic operation on FP8 data elements in that data element position in FP8 format and store a result of each arithmetic operation into a corresponding data element position of the identified packed data destination operand.
Abstract:
Disclosed embodiments relate to computing dot products of nibbles in tile operands. In one example, a processor includes decode circuitry to decode a tile dot product instruction having fields for an opcode, a destination identifier to identify a M by N destination matrix, a first source identifier to identify a M by K first source matrix, and a second source identifier to identify a K by N second source matrix, each of the matrices containing doubleword elements, and execution circuitry to execute the decoded instruction to perform a flow K times for each element (m, n) of the specified destination matrix to generate eight products by multiplying each nibble of a doubleword element (M,K) of the specified first source matrix by a corresponding nibble of a doubleword element (K,N) of the specified second source matrix, and to accumulate and saturate the eight products with previous contents of the doubleword element.
Abstract:
Techniques and mechanisms for processor circuitry to execute a load and expand instruction of an instruction set to generate decompressed matrix data. In an embodiment, the instruction comprises a source operand which indicates a location from which compressed matrix data, and corresponding metadata, are to be accessed. A destination operand of the instruction indicates a location which is to receive decompressed metadata, which is generated, during execution of the instruction, based on the compressed matrix data and the corresponding metadata. The metadata comprises compression mask information which identifies which elements of the matrix have been masked from the compressed matrix data. In another embodiment, the instruction further comprises a count operand which identifies a total number of the unmasked matrix elements which are represented in the compressed matrix data.
Abstract:
Embodiments detailed herein relate to matrix (tile) operations. For example, decode circuitry to decode an instruction having fields for an opcode and a memory address, and execution circuitry to execute the decoded instruction to store configuration information about usage of storage for two-dimensional data structures at the memory address.