公开(公告)号：US11812599B2

公开(公告)日：2023-11-07

申请号：US17670248

申请日：2022-02-11

Applicant: Intel Corporation

Inventor： Abhishek Sharma ,  Noriyuki Sato ,  Sarah Atanasov ,  Huseyin Ekin Sumbul ,  Gregory K. Chen ,  Phil Knag ,  Ram Krishnamurthy ,  Hui Jae Yoo ,  Van H. Le

IPC: G11C8/00 ,  H10B12/00 ,  H01L27/12 ,  G11C11/4096

CPC classification number: H10B12/00 ,  G11C11/4096 ,  H01L27/124 ,  H01L27/1207 ,  H01L27/1225 ,  H01L27/1255 ,  H01L27/1266

Abstract: Examples herein relate to a memory device comprising an eDRAM memory cell, the eDRAM memory cell can include a write circuit formed at least partially over a storage cell and a read circuit formed at least partially under the storage cell; a compute near memory device bonded to the memory device; a processor; and an interface from the memory device to the processor. In some examples, circuitry is included to provide an output of the memory device to emulate output read rate of an SRAM memory device comprises one or more of: a controller, a multiplexer, or a register. Bonding of a surface of the memory device can be made to a compute near memory device or other circuitry. In some examples, a layer with read circuitry can be bonded to a layer with storage cells. Any layers can be bonded together using techniques described herein.

公开(公告)号：US11727260B2

公开(公告)日：2023-08-15

申请号：US17484828

申请日：2021-09-24

Applicant: Intel Corporation

Inventor： Abhishek Sharma ,  Jack T. Kavalieros ,  Ian A. Young ,  Ram Krishnamurthy ,  Sasikanth Manipatruni ,  Uygar Avci ,  Gregory K. Chen ,  Amrita Mathuriya ,  Raghavan Kumar ,  Phil Knag ,  Huseyin Ekin Sumbul ,  Nazila Haratipour ,  Van H. Le

IPC: G06N3/063 ,  H01L27/108 ,  H01L27/11502 ,  G06N3/04 ,  G06F17/16 ,  H01L27/11 ,  G11C11/54 ,  G11C7/10 ,  G11C11/419 ,  G11C11/409 ,  G11C11/22 ,  G06N3/065 ,  H10B10/00 ,  H10B12/00 ,  H10B53/00

CPC classification number: G06N3/065 ,  G06F17/16 ,  G06N3/04 ,  G11C7/1006 ,  G11C7/1039 ,  G11C11/54 ,  H10B10/18 ,  H10B12/01 ,  H10B12/033 ,  H10B12/20 ,  H10B12/50 ,  H10B53/00 ,  G11C11/221 ,  G11C11/409 ,  G11C11/419

Abstract: An apparatus is described. The apparatus includes a compute-in-memory (CIM) circuit for implementing a neural network disposed on a semiconductor chip. The CIM circuit includes a mathematical computation circuit coupled to a memory array. The memory array includes an embedded dynamic random access memory (eDRAM) memory array. Another apparatus is described. The apparatus includes a compute-in-memory (CIM) circuit for implementing a neural network disposed on a semiconductor chip. The CIM circuit includes a mathematical computation circuit coupled to a memory array. The mathematical computation circuit includes a switched capacitor circuit. The switched capacitor circuit includes a back-end-of-line (BEOL) capacitor coupled to a thin film transistor within the metal/dielectric layers of the semiconductor chip. Another apparatus is described. The apparatus includes a compute-in-memory (CIM) circuit for implementing a neural network disposed on a semiconductor chip. The CIM circuit includes a mathematical computation circuit coupled to a memory array. The mathematical computation circuit includes an accumulation circuit. The accumulation circuit includes a ferroelectric BEOL capacitor to store a value to be accumulated with other values stored by other ferroelectric BEOL capacitors.

公开(公告)号：US11416165B2

公开(公告)日：2022-08-16

申请号：US16160482

申请日：2018-10-15

Applicant: INTEL CORPORATION

Inventor： Amrita Mathuriya ,  Sasikanth Manipatruni ,  Victor Lee ,  Huseyin Sumbul ,  Gregory Chen ,  Raghavan Kumar ,  Phil Knag ,  Ram Krishnamurthy ,  Ian Young ,  Abhishek Sharma

IPC: G06F12/00 ,  G06F3/06 ,  G06F12/1081 ,  G06N3/04 ,  G06F12/0802 ,  G06N3/063 ,  G06F12/0875 ,  G06F12/0897

Abstract: The present disclosure is directed to systems and methods of implementing a neural network using in-memory, bit-serial, mathematical operations performed by a pipelined SRAM architecture (bit-serial PISA) circuitry disposed in on-chip processor memory circuitry. The on-chip processor memory circuitry may include processor last level cache (LLC) circuitry. The bit-serial PISA circuitry is coupled to PISA memory circuitry via a relatively high-bandwidth connection to beneficially facilitate the storage and retrieval of layer weights by the bit-serial PISA circuitry during execution. Direct memory access (DMA) circuitry transfers the neural network model and input data from system memory to the bit-serial PISA memory and also transfers output data from the PISA memory circuitry to system memory circuitry. Thus, the systems and methods described herein beneficially leverage the on-chip processor memory circuitry to perform a relatively large number of vector/tensor calculations without burdening the processor circuitry.

公开(公告)号：US11195079B2

公开(公告)日：2021-12-07

申请号：US15821123

申请日：2017-11-22

Applicant: INTEL CORPORATION

Inventor： Huseyin E. Sumbul ,  Gregory K. Chen ,  Phil Knag ,  Raghavan Kumar ,  Ram K. Krishnamurthy

IPC: G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06N3/06

Abstract: In one embodiment, a processor comprises a first neuro-synaptic core comprising first circuitry to configure the first neuro-synaptic core as a neuron core responsive to a first value specified by a configuration parameter; and configure the first neuro-synaptic core as a synapse core responsive to a second value specified by the configuration parameter.

公开(公告)号：US10922607B2

公开(公告)日：2021-02-16

申请号：US15394976

申请日：2016-12-30

Applicant: Intel Corporation

Inventor： Abhronil Sengupta ,  Gregory K. Chen ,  Raghavan Kumar ,  Huseyin Ekin Sumbul ,  Phil Knag

IPC: G06N3/08 ,  G06N3/04

Abstract: In one embodiment, a processor is to store a membrane potential of a neural unit of a neural network; and calculate, at a particular time-step of the neural network, a change to the membrane potential of the neural unit occurring over multiple time-steps that have elapsed since the last time-step at which the membrane potential was updated, wherein each of the multiple time-steps that have elapsed since the last time-step is associated with at least one input to the neural unit that affects the membrane potential of the neural unit.

公开(公告)号：US10831446B2

公开(公告)日：2020-11-10

申请号：US16145569

申请日：2018-09-28

Applicant: Intel Corporation

Inventor： Gregory K. Chen ,  Raghavan Kumar ,  Huseyin Ekin Sumbul ,  Phil Knag ,  Ram Krishnamurthy ,  Sasikanth Manipatruni ,  Amrita Mathuriya ,  Abhishek Sharma ,  Ian A. Young

IPC: G06F7/544 ,  G06F17/16 ,  G06F7/501 ,  G06F9/30 ,  G06N3/04 ,  G06N3/063

Abstract: A memory device that includes a plurality subarrays of memory cells to store static weights and a plurality of digital full-adder circuits between subarrays of memory cells is provided. The digital full-adder circuit in the memory device eliminates the need to move data from a memory device to a processor to perform machine learning calculations. Rows of full-adder circuits are distributed between sub-arrays of memory cells to increase the effective memory bandwidth and reduce the time to perform matrix-vector multiplications in the memory device by performing bit-serial dot-product primitives in the form of accumulating m 1-bit×n-bit multiplications.

公开(公告)号：US20190043560A1

公开(公告)日：2019-02-07

申请号：US16146473

申请日：2018-09-28

Applicant: Intel Corporation

Inventor： Huseyin Ekin Sumbul ,  Gregory K. Chen ,  Raghavan Kumar ,  Phil Knag ,  Abhishek Sharma ,  Sasikanth Manipatruni ,  Amrita Mathuriya ,  Ram Krishnamurthy ,  Ian A. Young

IPC: G11C11/418 ,  G06F7/544 ,  G06F9/30 ,  G11C13/00 ,  G11C11/419

Abstract: A memory circuit has compute-in-memory circuitry that enables a multiply-accumulate (MAC) operation based on shared charge. Row access circuitry drives multiple rows of a memory array to multiply a first data word with a second data word stored in the memory array. The row access circuitry drives the multiple rows based on the bit pattern of the first data word. Column access circuitry drives a column of the memory array when the rows are driven. Accessed rows discharge the column line in an accumulative fashion. Sensing circuitry can sense voltage on the column line. A processor in the memory circuit computes a MAC value based on the voltage sensed on the column.

公开(公告)号：US11790217B2

公开(公告)日：2023-10-17

申请号：US16583201

申请日：2019-09-25

Applicant: Intel Corporation

Inventor： Ram Krishnamurthy ,  Gregory K. Chen ,  Raghavan Kumar ,  Phil Knag ,  Huseyin Ekin Sumbul

IPC: G06N3/063 ,  G06F7/523 ,  G06F9/38 ,  G06F9/30 ,  G06F7/509 ,  G06F7/544

CPC classification number: G06N3/063 ,  G06F7/5095 ,  G06F7/523 ,  G06F7/5443 ,  G06F9/30098 ,  G06F9/3893

Abstract: An apparatus is described. The apparatus includes a long short term memory (LSTM) circuit having a multiply accumulate circuit (MAC). The MAC circuit has circuitry to rely on a stored product term rather than explicitly perform a multiplication operation to determine the product term if an accumulation of differences between consecutive, preceding input values has not reached a threshold.

公开(公告)号：US11522012B2

公开(公告)日：2022-12-06

申请号：US16147091

申请日：2018-09-28

Applicant: Intel Corporation

Inventor： Jack T. Kavalieros ,  Ian A. Young ,  Ram Krishnamurthy ,  Ravi Pillarisetty ,  Sasikanth Manipatruni ,  Gregory Chen ,  Hui Jae Yoo ,  Van H. Le ,  Abhishek Sharma ,  Raghavan Kumar ,  Huichu Liu ,  Phil Knag ,  Huseyin Sumbul

IPC: H01L27/24 ,  G11C13/00 ,  H01L45/00 ,  H01L29/51

Abstract: A DIMA semiconductor structure is disclosed. The DIMA semiconductor structure includes a frontend including a semiconductor substrate, a transistor switch of a memory cell coupled to the semiconductor substrate and a computation circuit on the periphery of the frontend coupled to the semiconductor substrate. Additionally, the DIMA includes a backend that includes an RRAM component of the memory cell that is coupled to the transistor switch.

公开(公告)号：US11502696B2

公开(公告)日：2022-11-15

申请号：US16160800

申请日：2018-10-15

Applicant: Intel Corporation

Inventor： Amrita Mathuriya ,  Sasikanth Manipatruni ,  Victor Lee ,  Huseyin Sumbul ,  Gregory Chen ,  Raghavan Kumar ,  Phil Knag ,  Ram Krishnamurthy ,  Ian Young ,  Abhishek Sharma

IPC: H03M1/00 ,  H03M1/12 ,  G06N3/063 ,  G06N3/04 ,  G06N3/08

Abstract: Embodiments are directed to systems and methods of implementing an analog neural network using a pipelined SRAM architecture (“PISA”) circuitry disposed in on-chip processor memory circuitry. The on-chip processor memory circuitry may include processor last level cache (LLC) circuitry. One or more physical parameters, such as a stored charge or voltage, may be used to permit the generation of an in-memory analog output using a SRAM array. The generation of an in-memory analog output using only word-line and bit-line capabilities beneficially increases the computational density of the PISA circuit without increasing power requirements. Thus, the systems and methods described herein beneficially leverage the existing capabilities of on-chip SRAM processor memory circuitry to perform a relatively large number of analog vector/tensor calculations associated with execution of a neural network, such as a recurrent neural network, without burdening the processor circuitry and without significant impact to the processor power requirements.