Intel Corporation

University of Michigan

Himanshu Kaul

Mark A Anders

Sanu K Mathew

Ram K Krishnamurthy

Gregory Kengho Chen

Abhishek R Appu

Joydeep Ray

Altug Koker

Balaji Vembu

Vasanth Ranganathan

Sanjeev S Jahagirdar

Kamal Sinha

Anbang Yao

Eriko Nurvitadhi

Nicolas C Galoppo Von Borries

Tatiana Shpeisman

Xiaoming Chen

Rajkishore Barik

Ping Tak Tang

Tsung-Han Lin

Michael S Strickland

Sudhir K Satpathy

Dennis Sylvester

Vivek K De

Jesse R Walker

Kevin Lai Lin

David Theodore Blaauw

Mauro J Kobrinsky

Shekhar Yeshwant Borkar

Vikram B Suresh

Steven Hsu

William C Hasenplaugh

Amit Agarwal

Jae-sun Seo

Jason Sandri

Randy L Allmon

Yejoong Kim

Shekhar Y Bokar

Jonathan Enoch

Mark Anders

SeongJong Kim

Seongjong Kim

Aaron T Stillmaker

Embodiments disclosed herein include interconnect layers that include non-uniform interconnect heights and methods of forming such devices. In an embodiment, an interconnect layer comprises an interlayer dielectric (ILD), a first interconnect disposed in the ILD, wherein the first interconnect has a first height, and a second interconnect disposed in the ILD, wherein the second interconnect has a second height that is different than the first height.

Multi-height interconnect trenches for resistance and capacitance optimization

One embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (SIMT) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute an intermediate product of 16-bit operands and to compute a 32-bit sum based on the intermediate product.

Instructions and logic to perform floating point and integer operations for machine learning

One embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (SIMT) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute a 32-bit intermediate product of 16-bit operands and to compute a 32-bit sum based on the 32-bit intermediate product.

An apparatus includes a first port set that includes an input port and an output port. The apparatus further includes a plurality of second port sets. Each of the second port sets includes an input port coupled to the output port of the first port set and an output port coupled to the input port of the first port set. The plurality of second port sets are to each communicate at a first maximum bandwidth and the first port set is to communicate at a second maximum bandwidth that is higher than the first maximum bandwidth.

High bandwidth core to network-on-chip interface

One embodiment provides a graphics processor comprising a memory controller and a graphics processing resource coupled with the memory controller. The graphics processing resource includes circuitry configured to execute an instruction to perform a matrix operation on first input including weight data and second input including input activation data, generate intermediate data based on a result of the matrix operation, quantize the intermediate data to a floating-point format determined based on a statistical distribution of first output data, and output, as second output data, quantized intermediate data in a determined floating-point format.

A processing apparatus is provided comprising a multiprocessor having a multithreaded architecture. The multiprocessor can execute at least one single instruction to perform parallel mixed precision matrix operations. In one embodiment the apparatus includes a memory interface and an array of multiprocessors coupled to the memory interface. At least one multiprocessor in the array of multiprocessors is configured to execute a fused multiply-add instruction in parallel across multiple threads.

Systems, apparatuses and methods may provide for technology that conduct a first alignment between a plurality of floating-point numbers based on a first subset of exponent bits. The technology may also conduct, at least partially in parallel with the first alignment, a second alignment between the plurality of floating-point numbers based on a second subset of exponent bits, where the first subset of exponent bits are LSBs and the second subset of exponent bits are MSBs. In one example, technology adds the aligned plurality of floating-point numbers to one another. With regard to the second alignment, the technology may also identify individual exponents of a plurality of floating-point numbers, identify a maximum exponent across the individual exponents, and conduct a subtraction of the individual exponents from the maximum exponent, where the subtraction is conducted from MSB to LSB.

Floating-point dot-product hardware with wide multiply-adder tree for machine learning accelerators

Instructions and logic to perform floating-point and integer operations for machine learning

Some embodiments include apparatuses having a first circuit path including drive units coupled in series between a first node and a first additional node, a second circuit path including drive units coupled in series between a second node and a second additional node, each drive unit of the driver units of the first circuit path and the second circuit path including an inverter, and a transmission gate circuit including an input node and an output node coupled to an input node and an output node, respectively, of the inverter; and control circuitry to provide control information to the transmission gate circuit of each of the driver units of the first circuit path and the second circuit path.

Reconfigurable interconnect structure in integrated circuits

A configurable integrated circuit to compute vector dot products between a first N-bit vector and a second N-bit vector in a plurality of precision modes. An embodiment includes M slices, each of which calculates the vector dot products between a corresponding segment of the first and the second N-bit vectors. Each of the slices outputs intermediary multiplier results for the lower precision modes, but not for highest precision mode. A plurality of adder trees to sum up the plurality of intermediate multiplier results, with each adder tree producing a respective adder out result. An accumulator to merge the adder out result from a first adder tree with the adder out result from a second adder tree to produce the vector dot product of the first and the second N-bit vector in the highest precision mode.

Reconfigurable multi-precision integer dot-product hardware accelerator for machine-learning applications

Disclosed embodiments relate to a variable format, variable sparsity matrix multiplication (VFVSMM) instruction. In one example, a processor includes fetch and decode circuitry to fetch and decode a VFVSMM instruction specifying locations of A, B, and C matrices having (M&#xd7;K), (K&#xd7;N), and (M&#xd7;N) elements, respectively, execution circuitry, responsive to the decoded VFVSMM instruction, to: route each row of the specified A matrix, staggering subsequent rows, into corresponding rows of a (M&#xd7;N) processing array, and route each column of the specified B matrix, staggering subsequent columns, into corresponding columns of the processing array, wherein each of the processing units is to generate K products of A-matrix elements and matching B-matrix elements having a same row address as a column address of the A-matrix element, and to accumulate each generated product with a corresponding C-matrix element.

Variable format, variable sparsity matrix multiplication instruction

One embodiment provides for a machine-learning hardware accelerator comprising a compute unit having an adder and a multiplier that are shared between integer data path and a floating-point datapath, the upper bits of input operands to the multiplier to be gated during floating-point operation.

Growing community of inventors

Portland, OR, United States of America

Himanshu Kaul