Intel Corporation

Michael J Espig

Christopher J Hughes

Robert Valentine

Elmoustapha Ould-Ahmed-Vall

Dan Baum

Mark Jay Charney

Alexander F Heinecke

Raanan Sade

Bret L Toll

Ronen Zohar

Vinodh Gopal

Wajdi K Feghali

James D Guilford

Ravishankar Iyer

Zhen Fang

Aditya Varma

David J Harriman

Mahesh U Wagh

Jasmin Ajanovic

Chen Koren

Deepti Aggarwal

Prasoonkumar Surti

Subramaniam M Maiyuran

Varghese George

Tsung-Yuan Charles Tai

Guei-Yuan Lueh

Ren Wang

Christian Maciocco

Lakshman Krishnamurthy

Victor W Lee

Robert S Chappell

Daehyun Kim

Jorge E Parra

Michael W Leddige

Jonathan D Pearce

Mahesh K Kumashikar

Sumit Mohan

Rajendra S Yavatkar

Sadagopan Srinivasan

Henry Stracovsky

Bin Li

Richard R Winterton

Chekib Nouira

Prakaram Joshi

Jongsoo Park

Ould-Ahmed-Vall Elmoustapha

An embodiment of an apparatus comprises one or more fractional width fused multiply-accumulate (FMA) circuits configured as a shared Wallace tree, and circuitry coupled to the one or more fractional width FMA circuits to provide one or more fractional width FMA operations through the one or more fractional width FMA circuits. Other embodiments are disclosed and claimed.

Compressed wallace trees in FMA circuits

Disclosed embodiments relate to accelerating multiplication of sparse matrices. In one example, a processor is to fetch and decode an instruction having fields to specify locations of first, second, and third matrices, and an opcode indicating the processor is to multiply and accumulate matching non-zero (NZ) elements of the first and second matrices with corresponding elements of the third matrix, and executing the decoded instruction as per the opcode to generate NZ bitmasks for the first and second matrices, broadcast up to two NZ elements at a time from each row of the first matrix and each column of the second matrix to a processing engine (PE) grid, each PE to multiply and accumulate matching NZ elements of the first and second matrices with corresponding elements of the third matrix. Each PE further to store an NZ element for use in a subsequent multiplications.

Systems and methods of instructions to accelerate multiplication of sparse matrices using bitmasks that identify non-zero elements

Systems, methods, and apparatuses to support packed data convolution instructions with shift control and width control are described. In one embodiment, a hardware processor includes a decoder circuit to decode a single instruction into a decoded single instruction, the single instruction having fields that identify a first packed data source, a second packed data source, a packed data destination, a sliding window width, and a stride, and an opcode that indicates an execution circuit is to generate a first chunk of contiguous elements of the first packed data source having a width of the sliding window width, generate a second chunk of contiguous elements of the first packed data source having the width of the sliding window width and shifted by the stride, multiply each element of the first chunk by a corresponding element of a respective chunk of the second packed data source to generate a first set of products, add the first set of products together to generate a first sum, multiply each element of the second chunk by a corresponding element of a respective chunk of the second packed data source to generate a second set of products, add the second set of products together to generate a second sum, and store the first sum in a first element of the packed data destination and the second sum in a second element of the packed data destination; and the execution circuit is to execute the decoded single instruction according to the opcode.

Apparatuses, methods, and systems for a packed data convolution instruction with shift control and width control

Disclosed embodiments relate to matrix compress/decompress instructions. In one example, a processor includes fetch circuitry to fetch a compress instruction having a format with fields to specify an opcode and locations of decompressed source and compressed destination matrices, decode circuitry to decode the fetched compress instructions, and execution circuitry, responsive to the decoded compress instruction, to: generate a compressed result according to a compress algorithm by compressing the specified decompressed source matrix by either packing non-zero-valued elements together and storing the matrix position of each non-zero-valued element in a header, or using fewer bits to represent one or more elements and using the header to identify matrix elements being represented by fewer bits; and store the compressed result to the specified compressed destination matrix.

Systems and methods for performing matrix compress and decompress instructions

In an embodiment, a processor includes: a fetch circuit to fetch instructions, the instructions including a sum of squared differences (SSD) instruction; a decode circuit to decode the SSD instruction; and an execution circuit to, during an execution of the decoded SSD instruction, generate an SSD output vector based on a plurality of input vectors, the SSD output vector including a plurality of squared differences values. Other embodiments are described and claimed.

Instruction and logic for sum of square differences

Disclosed embodiments relate to systems and methods for performing nibble-sized operations on matrix elements. In one example, a processor includes fetch circuitry to fetch an instruction, decode circuitry to decode the fetched instruction the fetched instruction having fields to specify an opcode and locations of first source, second source, and destination matrices, the opcode to indicate the processor is to, for each pair of corresponding elements of the first and second source matrices, logically partition each element into nibble-sized partitions, perform an operation indicated by the instruction on each partition, and store execution results to a corresponding nibble-sized partition of a corresponding element of the destination matrix. The exemplary processor includes execution circuitry to execute the decoded instruction as per the opcode.

Systems and methods for performing nibble-sized operations on matrix elements

An apparatus and method for efficiently performing a multiply add or multiply accumulate operation. For example, one embodiment of a processor comprises: a decoder to decode an instruction specifying an operation, the instruction comprising a first operand identifying a multiplier and a second operand identifying a multiplicand; and fused multiply-add (FMA) execution circuitry comprising first multiplication circuitry to perform a multiplication using the multiplicand and multiplier to generate a result for multipliers and multiplicands falling within a first precision range, and second multiplication circuitry to be used instead of the first multiplication circuitry for multipliers and multiplicands falling within a second precision range.

Method and apparatus for efficient binary and ternary support in fused multiply-add (FMA) circuits

Disclosed embodiments relate to instructions for fast element unpacking. In one example, a processor includes fetch circuitry to fetch an instruction whose format includes fields to specify an opcode and locations of an Array-of-Structures (AOS) source matrix and one or more Structure of Arrays (SOA) destination matrices, wherein: the specified opcode calls for unpacking elements of the specified AOS source matrix into the specified Structure of Arrays (SOA) destination matrices, the AOS source matrix is to contain N structures each containing K elements of different types, with same-typed elements in consecutive structures separated by a stride, the SOA destination matrices together contain K segregated groups, each containing N same-typed elements, decode circuitry to decode the fetched instruction, and execution circuitry, responsive to the decoded instruction, to unpack each element of the specified AOS matrix into one of the K element types of the one or more SOA matrices.

Systems for performing instructions for fast element unpacking into 2-dimensional registers

Methods and apparatus for approximation using polynomial functions are disclosed. In one embodiment, a processor comprises decoding and execution circuitry. The decoding circuitry is to decode an instruction, where the instruction comprises a first operand specifying an output location and a second operand specifying a plurality of data element values to be computed. The execution circuitry is to execute the decoded instruction. The execution includes to compute a result for each of the plurality of data element values using a polynomial function to approximate a complex function, where the computation uses coefficients stored in a lookup location for the complex function, and where data element values within different data element value ranges use different sets of coefficients. The execution further includes to store results of the computation in the output location.

Method and apparatus for approximation using polynomials

Disclosed embodiments relate to systems and methods for performing duplicate detection instructions on two-dimensional (2D) data. In one example, a processor includes fetch circuitry to fetch an instruction, decode circuitry to decode the fetched instruction having fields to specify an opcode and locations of a source matrix comprising M&#xd7;N elements and a destination, the opcode to indicate execution circuitry is to use a plurality of comparators to discover duplicates in the source matrix, and store indications of locations of discovered duplicates in the destination. The execution circuitry to execute the decoded instruction as per the opcode.

Systems and methods for performing duplicate detection instructions on 2D data

Systems, methods, and apparatuses relating to performing fast Fourier transform (FFT) configuration and computation operations are described. In one embodiment, a processor includes a matrix operations accelerator circuit that includes a two-dimensional grid of processing element circuits; a first plurality of registers that represents a first two-dimensional matrix coupled to the matrix operations accelerator circuit; a second plurality of registers that represents a second two-dimensional matrix coupled to the matrix operations accelerator circuit; a decoder, of a core coupled to the matrix operations accelerator circuit, to decode a single instruction into a decoded single instruction; and an execution circuit of the core to execute the decoded single instruction to cause the two-dimensional grid of processing element circuits to operate on a first packed data input value and a first complex twiddle factor value to produce a first result and a second result.

Apparatuses, methods, and systems for fast fourier transform configuration and computation instructions

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Portland, OR, United States of America

Michael J Espig