公开(公告)号：US20210117197A1

公开(公告)日：2021-04-22

申请号：US17132895

申请日：2020-12-23

Applicant: Intel Corporation

Inventor： Steven Hsu ,  Amit Agarwal ,  Debabrata Mohapatra ,  Arnab Raha ,  Moongon Jung ,  Gautham Chinya ,  Ram Krishnamurthy

IPC: G06F9/30 ,  G06F13/16 ,  G06F15/78 ,  G06N3/04

Abstract: Systems, apparatuses and methods identify a plurality of registers that are associated with a system-on-chip. The plurality of registers includes a first portion dedicated to write operations and a second portion dedicated to read operations. The technology writes data to the first portion of the plurality of registers, and transfers the data from the first portion to the second portion.

公开(公告)号：US20240220785A1

公开(公告)日：2024-07-04

申请号：US18408716

申请日：2024-01-10

Applicant: Intel Corporation

Inventor： Gautham Chinya ,  Huichu Liu ,  Arnab Raha ,  Debabrata Mohapatra ,  Cormac Brick ,  Lance Hacking

IPC: G06N3/063 ,  G06F9/38 ,  G06F9/448 ,  G06F9/50 ,  G06N5/04

CPC classification number: G06N3/063 ,  G06F9/3814 ,  G06F9/3877 ,  G06F9/4498 ,  G06F9/5027 ,  G06N5/04

Abstract: Methods and systems include a neural network system that includes a neural network accelerator comprising. The neural network accelerator includes multiple processing engines coupled together to perform arithmetic operations in support of an inference performed using the deep neural network system. The neural network accelerator also includes a schedule-aware tensor data distribution circuitry or software that is configured to load tensor data into the multiple processing engines in a load phase, extract output data from the multiple processing engines in an extraction phase, reorganize the extracted output data, and store the reorganized extracted output data to memory.

公开(公告)号：US11907827B2

公开(公告)日：2024-02-20

申请号：US16456707

申请日：2019-06-28

Applicant: Intel Corporation

Inventor： Gautham Chinya ,  Huichu Liu ,  Arnab Raha ,  Debabrata Mohapatra ,  Cormac Brick ,  Lance Hacking

IPC: G06N3/063 ,  G06N5/04 ,  G06F9/448 ,  G06F9/38 ,  G06F9/50

CPC classification number: G06N3/063 ,  G06F9/3814 ,  G06F9/3877 ,  G06F9/4498 ,  G06F9/5027 ,  G06N5/04

Abstract: Methods and systems include a neural network system that includes a neural network accelerator. The neural network accelerator includes multiple processing engines coupled together to perform arithmetic operations in support of an inference performed using the deep neural network system. The neural network accelerator also includes a schedule-aware tensor data distribution circuitry or software that is configured to load tensor data into the multiple processing engines in a load phase, extract output data from the multiple processing engines in an extraction phase, reorganize the extracted output data, and store the reorganized extracted output data to memory.

公开(公告)号：US11804851B2

公开(公告)日：2023-10-31

申请号：US16832804

申请日：2020-03-27

Applicant: Intel Corporation

Inventor： Gautham Chinya ,  Debabrata Mohapatra ,  Arnab Raha ,  Huichu Liu ,  Cormac Brick

IPC: H03M7/30 ,  G06F16/22 ,  G06N3/063 ,  G06N3/08 ,  G06N3/04

CPC classification number: H03M7/3082 ,  G06F16/2237 ,  G06N3/063 ,  G06N3/04 ,  G06N3/08

Abstract: Methods, systems, articles of manufacture, and apparatus are disclosed to decode zero-value-compression data vectors. An example apparatus includes: a buffer monitor to monitor a buffer for a header including a value indicative of compressed data; a data controller to, when the buffer includes compressed data, determine a first value of a sparse select signal based on (1) a select signal and (2) a first position in a sparsity bitmap, the first value of the sparse select signal corresponding to a processing element that is to process a portion of the compressed data; and a write controller to, when the buffer includes compressed data, determine a second value of a write enable signal based on (1) the select signal and (2) a second position in the sparsity bitmap, the second value of the write enable signal corresponding to the processing element that is to process the portion of the compressed data.

公开(公告)号：US20220188075A1

公开(公告)日：2022-06-16

申请号：US17688131

申请日：2022-03-07

Applicant: Intel Corporation

Inventor： Arnab Raha ,  Mark A. Anders ,  Raymond Jit-Hung Sung ,  Debabrata Mohapatra ,  Deepak Abraham Mathaikutty ,  Ram K. Krishnamurthy ,  Himanshu Kaul

IPC: G06F7/544 ,  G06F7/483 ,  G06N3/08

Abstract: A FPMAC operation has two operands: an input operand and a weight operand. The operands may have a format of FP16, BF16, or INT8. Each operand is split into two portions. The two portions are stored in separate storage units. Then operands are transferred to register files of a PE, with each register file storing bits of an operand sequentially. The PE performs the FPMAC operation based on the operands. The PE may include an FPMAC unit configured to compute an individual partial sum of the PE. The PE may also include an FP adder to accumulate the individual partial sum with other data, such as an output from another PE or an output form another PE array. The FP adder may be fused with the FPMAC unit in a single circuit that can do speculative alignment and has separate critical paths for alignment and normalization.

公开(公告)号：US20210397414A1

公开(公告)日：2021-12-23

申请号：US17358868

申请日：2021-06-25

Applicant: Intel Corporation

Inventor： Arnab Raha ,  Mark A. Anders ,  Martin Power ,  Martin Langhammer ,  Himanshu Kaul ,  Debabrata Mohapatra ,  Gautham Chinya ,  Cormac Brick ,  Ram Krishnamurthy

IPC: G06F7/544 ,  G06F7/527 ,  G06F5/01

Abstract: Systems, apparatuses and methods may provide for multi-precision multiply-accumulate (MAC) technology that includes a plurality of arithmetic blocks, wherein the plurality of arithmetic blocks each contain multiple multipliers, and wherein the logic is to combine multipliers one or more of within each arithmetic block or across multiple arithmetic blocks. In one example, one or more intermediate multipliers are of a size that is less than precisions supported by arithmetic blocks containing the one or more intermediate multipliers.

公开(公告)号：US20210326144A1

公开(公告)日：2021-10-21

申请号：US17359392

申请日：2021-06-25

Applicant: Intel Corporation

Inventor： Arnab Raha ,  Deepak Mathaikutty ,  Debabrata Mohapatra ,  Sang Kyun Kim ,  Gautham Chinya ,  Cormac Brick

IPC: G06F9/445 ,  G06F9/50 ,  G06F9/30 ,  G06N20/00 ,  H03K19/177 ,  H03K19/20

Abstract: Methods, apparatus, systems, and articles of manufacture to load data into an accelerator are disclosed. An example apparatus includes data provider circuitry to load a first section and an additional amount of compressed machine learning parameter data into a processor engine. Processor engine circuitry executes a machine learning operation using the first section of compressed machine learning parameter data. A compressed local data re-user circuitry determines if a second section is present in the additional amount of compressed machine learning parameter data. The processor engine circuitry executes a machine learning operation using the second section when the second section is present in the additional amount of compressed machine learning parameter data.

公开(公告)号：US20200228137A1

公开(公告)日：2020-07-16

申请号：US16832804

申请日：2020-03-27

Applicant: Intel Corporation

Inventor： Gautham Chinya ,  Debabrata Mohapatra ,  Arnab Raha ,  Huichu Liu ,  Cormac Brick

IPC: H03M7/30 ,  G06F16/22 ,  G06N3/063

Abstract: Methods, systems, articles of manufacture, and apparatus are disclosed to decode zero-value-compression data vectors. An example apparatus includes: a buffer monitor to monitor a buffer for a header including a value indicative of compressed data; a data controller to, when the buffer includes compressed data, determine a first value of a sparse select signal based on (1) a select signal and (2) a first position in a sparsity bitmap, the first value of the sparse select signal corresponding to a processing element that is to process a portion of the compressed data; and a write controller to, when the buffer includes compressed data, determine a second value of a write enable signal based on (1) the select signal and (2) a second position in the sparsity bitmap, the second value of the write enable signal corresponding to the processing element that is to process the portion of the compressed data.

公开(公告)号：US12229673B2

公开(公告)日：2025-02-18

申请号：US17524333

申请日：2021-11-11

Applicant: Intel Corporation

Inventor： Deepak Mathaikutty ,  Arnab Raha ,  Raymond Sung ,  Debabrata Mohapatra ,  Cormac Brick

IPC: H03M7/00 ,  G06N3/08 ,  G06N5/04 ,  H03M7/30

Abstract: Systems, apparatuses and methods may provide for technology that prefetches compressed data and a sparsity bitmap from a memory to store the compressed data in a decode buffer, where the compressed data is associated with a plurality of tensors, wherein the compressed data is in a compressed format. The technology aligns the compressed data with the sparsity bitmap to generate decoded data, and provides the decoded data to a plurality of processing elements.

公开(公告)号：US12147836B2

公开(公告)日：2024-11-19

申请号：US17520281

申请日：2021-11-05

Applicant: INTEL CORPORATION

Inventor： Debabrata Mohapatra ,  Arnab Raha ,  Deepak Mathaikutty ,  Raymond Sung ,  Cormac Brick

IPC: G06F9/50 ,  G06F7/50 ,  G06F9/48 ,  G06F15/80 ,  G06F15/82 ,  G06N20/00

Abstract: Techniques and configurations enhancing the performance of hardware (HW) accelerators are provided. A schedule-aware, dynamically reconfigurable, tree-based partial sum accumulator architecture for HW accelerators is provided, where the depth of an adder tree in the HW accelerator is dynamically based on a dataflow schedule generated by a compiler. The adder tree depth is adjusted on a per-layer basis at runtime. Configuration registers, programmed via software, dynamically alter the adder tree depth for partial sum accumulation based on the dataflow schedule. By facilitating a variable depth adder tree during runtime, the compiler can choose a compute optimal dataflow schedule that minimizes the number of compute cycles needed to accumulate partial sums across multiple processing elements (PEs) within a PE array of a HW accelerator.