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1. 发明申请

US20200227474A1 SPIN ORBIT TORQUE MEMORY DEVICES AND METHODS OF FABRICATION 审中-公开
公开(公告)号：US20200227474A1
公开(公告)日：2020-07-16
申请号：US16246360
申请日：2019-01-11
申请人： Kevin O'Brien , Christopher Wiegand , Tofizur Rahman , Noriyuki Sato , Gary Allen , James Pellegren , Angeline Smith , Tanay Gosavi , Sasikanth Manipatruni , Kaan Oguz , Benjamin Buford , Ian Young
发明人： Kevin O'Brien , Christopher Wiegand , Tofizur Rahman , Noriyuki Sato , Gary Allen , James Pellegren , Angeline Smith , Tanay Gosavi , Sasikanth Manipatruni , Kaan Oguz , Benjamin Buford , Ian Young
IPC分类号： H01L27/22 , H01L43/14 , H01L43/10 , H01L43/08 , H01L43/04
摘要： A perpendicular spin orbit memory device includes a first electrode having a magnetic material and platinum and a material layer stack on a portion of the first electrode. The material layer stack includes a free magnet, a fixed magnet above the first electrode, a tunnel barrier between the free magnet and the fixed magnet and a second electrode coupled with the fixed magnet.

2. 发明申请

US20200091414A1 MAGNETOELECTRIC SPIN ORBIT LOGIC BASED FULL ADDER 审中-公开
公开(公告)号：US20200091414A1
公开(公告)日：2020-03-19
申请号：US16130912
申请日：2018-09-13
申请人： Huichu Liu , Sasikanth Manipatruni , Daniel Morris , Kaushik Vaidyanathan , Tanay Karnik , Ian Young
发明人： Huichu Liu , Sasikanth Manipatruni , Daniel Morris , Kaushik Vaidyanathan , Tanay Karnik , Ian Young
IPC分类号： H01L43/04 , H01L43/06 , H01L43/10 , G11C11/16 , G11C11/18
摘要： An apparatus is provided which comprises a full adder including magnetoelectric material and spin orbit material. In some embodiments, the adder includes: a 3-bit carry generation structure and a multi-bit sum generation structure coupled to the 3-bit carry generation structure. In some embodiments, the 3-bit carry generation structure includes at least three cells comprising magnetoelectric material and spin orbit material, wherein the 3-bit carry generation structure is to perform a minority logic operation on first, second, and third inputs to generate a carry output. In some embodiments, the multi-bit sum generation structure includes at least four cells comprising magnetoelectric material and spin orbit material, wherein the multi-bit sum generation structure is to perform a minority logic operation on the first, second, and third inputs and the carry output to generate a sum output.

3. 发明申请

US20190057050A1 PIPELINE CIRCUIT ARCHITECTURE TO PROVIDE IN-MEMORY COMPUTATION FUNCTIONALITY 审中-公开
公开(公告)号：US20190057050A1
公开(公告)日：2019-02-21
申请号：US16160952
申请日：2018-10-15
申请人： Amrita Mathuriya , Sasikanth Manipatruni , Victor W. Lee , Abhishek Sharma , Huseyin E. Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , Ian Young
发明人： Amrita Mathuriya , Sasikanth Manipatruni , Victor W. Lee , Abhishek Sharma , Huseyin E. Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , Ian Young
IPC分类号： G06F13/16 , G06F13/28
摘要： Techniques and mechanisms for performing in-memory computations with circuitry having a pipeline architecture. In an embodiment, various stages of a pipeline each include a respective input interface and a respective output interface, distinct from said input interface, to couple to different respective circuitry. These stages each further include a respective array of memory cells and circuitry to perform operations based on data stored by said array. A result of one such in-memory computation may be communicated from one pipeline stage to a respective next pipeline stage for use in further in-memory computations. Control circuitry, interconnect circuitry, configuration circuitry or other logic of the pipeline precludes operation of the pipeline as a monolithic, general-purpose memory device. In other embodiments, stages of the pipeline each provide a different respective layer of a neural network.

4. 发明申请

US20190057727A1 MEMORY DEVICE TO PROVIDE IN-MEMORY COMPUTATION FUNCTIONALITY FOR A PIPELINE CIRCUIT ARCHITECTURE 审中-公开
公开(公告)号：US20190057727A1
公开(公告)日：2019-02-21
申请号：US16160955
申请日：2018-10-15
申请人： Amrita Mathuriya , Sasikanth Manipatruni , Victor W. Lee , Abhishek Sharma , Huseyin E. Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , Ian Young
发明人： Amrita Mathuriya , Sasikanth Manipatruni , Victor W. Lee , Abhishek Sharma , Huseyin E. Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , Ian Young
IPC分类号： G11C7/10 , G11C5/04 , G06F12/06
摘要： Techniques and mechanisms for configuring a memory device to perform a sequence of in-memory computations. In an embodiment, a memory device includes a memory array and circuitry, coupled thereto, to perform data computations based on the data stored at the memory array. Based on instructions received at the memory device, control circuitry is configured to enable an automatic performance of a sequence of operations. In another embodiment, the memory device is coupled in an in-series arrangement of other memory devices to provide a pipeline circuit architecture. The memory devices each function as a respective stage of the pipeline circuit architecture, where the stages each perform respective in-memory computations. Some or all such stages each provide a different respective layer of a neural network.

5. 发明申请

US20190057304A1 IN-MEMORY ANALOG NEURAL CACHE 审中-公开
公开(公告)号：US20190057304A1
公开(公告)日：2019-02-21
申请号：US16160800
申请日：2018-10-15
申请人： Amrita Mathuriya , Sasikanth Manipatruni , Victor Lee , Huseyin Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , IAN YOUNG , Abhishek Sharma
发明人： Amrita Mathuriya , Sasikanth Manipatruni , Victor Lee , Huseyin Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , IAN YOUNG , Abhishek Sharma
IPC分类号： G06N3/063 , G06N3/04
摘要： The present disclosure is 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.

6. 发明申请

US20190057300A1 WEIGHT PREFETCH FOR IN-MEMORY NEURAL NETWORK EXECUTION 审中-公开
公开(公告)号：US20190057300A1
公开(公告)日：2019-02-21
申请号：US16160466
申请日：2018-10-15
申请人： Amrita Mathuriya , Sasikanth Manipatruni , Victor Lee , Huseyin Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , IAN YOUNG , Abhishek Sharma
发明人： Amrita Mathuriya , Sasikanth Manipatruni , Victor Lee , Huseyin Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , IAN YOUNG , Abhishek Sharma
IPC分类号： G06N3/04 , G06F3/06 , G06F12/0875
摘要： The present disclosure is directed to systems and methods of bit-serial, in-memory, execution of at least an nth layer of a multi-layer neural network in a first on-chip processor memory circuitry portion contemporaneous with prefetching and storing layer weights associated with the (n+1)st layer of the multi-layer neural network in a second on-chip processor memory circuitry portion. The storage of layer weights in on-chip processor memory circuitry beneficially decreases the time required to transfer the layer weights upon execution of the (n+1)st layer of the multi-layer neural network by the first on-chip processor memory circuitry portion. In addition, the on-chip processor memory circuitry may include a third on-chip processor memory circuitry portion used to store intermediate and/or final input/output values associated with one or more layers included in the multi-layer neural network.

7. 发明申请

US20190057036A1 PROGRAMMABLE INTERFACE TO IN-MEMORY CACHE PROCESSOR 审中-公开
公开(公告)号：US20190057036A1
公开(公告)日：2019-02-21
申请号：US16160270
申请日：2018-10-15
申请人： Amrita Mathuriya , Sasikanth Manipatruni , Victor Lee , Huseyin Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , IAN YOUNG , Abhishek Sharma
发明人： Amrita Mathuriya , Sasikanth Manipatruni , Victor Lee , Huseyin Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , IAN YOUNG , Abhishek Sharma
IPC分类号： G06F12/0875 , G06N3/063 , G06N3/04 , G06F3/06
摘要： The present disclosure is directed to systems and methods of implementing a neural network using in-memory mathematical operations performed by pipelined SRAM architecture (PISA) circuitry disposed in on-chip processor memory circuitry. A high-level compiler may be provided to compile data representative of a multi-layer neural network model and one or more neural network data inputs from a first high-level programming language to an intermediate domain-specific language (DSL). A low-level compiler may be provided to compile the representative data from the intermediate DSL to multiple instruction sets in accordance with an instruction set architecture (ISA), such that each of the multiple instruction sets corresponds to a single respective layer of the multi-layer neural network model. Each of the multiple instruction sets may be assigned to a respective SRAM array of the PISA circuitry for in-memory execution. Thus, the systems and methods described herein beneficially leverage the on-chip processor memory circuitry to perform a relatively large number of in-memory vector/tensor calculations in furtherance of neural network processing without burdening the processor circuitry.

8. 发明申请

US20140091411A1 Repeated Spin Current Interconnects 有权
标题翻译：重复自旋电流互连
公开(公告)号：US20140091411A1
公开(公告)日：2014-04-03
申请号：US13630499
申请日：2012-09-28
申请人： Sasikanth Manipatruni , Dmitri Nikonov , Ian Young
发明人： Sasikanth Manipatruni , Dmitri Nikonov , Ian Young
IPC分类号： H01L29/82
CPC分类号： H01L43/02 , H01L27/22 , H01L27/222 , H01L43/00 , H01L2924/0002 , H01L2924/00
摘要： One embodiment includes a metal layer including first and second metal portions; a ferromagnetic layer including a first ferromagnetic portion that directly contacts the first metal portion and a second ferromagnetic portion that directly contacts the second metal portion; and a first metal non-magnetic interconnect coupling the first ferromagnetic portion to the second ferromagnetic portion. The spin interconnect conveys spin polarized current suitable for spin logic circuits. The interconnect may be included in a current repeater such as an inverter or buffer. The interconnect may perform regeneration of spin signals. Some embodiments extend spin interconnects into three dimensions (e.g., vertically across layers of a device) using vertical non-magnetic metal interconnects. Spin interconnects that can communicate spin current without repeated conversion of the current between spin and electrical signals enable spin logic circuits by reducing power requirements, reducing circuit size, and increasing circuit speed.
摘要翻译：一个实施例包括包括第一和第二金属部分的金属层; 包括直接接触第一金属部分的第一铁磁部分和直接接触第二金属部分的第二铁磁部分的铁磁层; 以及将第一铁磁部分耦合到第二铁磁部分的第一金属非磁性互连。自旋互连传送适用于自旋逻辑电路的自旋极化电流。互连可以包括在诸如逆变器或缓冲器的当前中继器中。互连可以执行自旋信号的再生。一些实施例使用垂直非磁性金属互连将自旋互连扩展成三维（例如，垂直跨设备的层）。无需重复转换自旋和电信号之间的电流的自旋电流的自旋互连可以通过降低功耗要求，降低电路尺寸和提高电路速度来实现自旋逻辑电路。

9. 发明申请

US20140001524A1 Spin Hall Effect Memory 有权
标题翻译：旋转霍尔效应记忆
公开(公告)号：US20140001524A1
公开(公告)日：2014-01-02
申请号：US13537541
申请日：2012-06-29
申请人： Sasikanth Manipatruni , Dmitri Nikonov , Ian Young
发明人： Sasikanth Manipatruni , Dmitri Nikonov , Ian Young
IPC分类号： H01L27/22
CPC分类号： H01L43/08 , G11C11/161 , G11C11/1655 , G11C11/1659 , G11C11/1673 , G11C11/1675 , G11C11/18 , H01L27/222 , H01L27/228 , H01L28/65
摘要： An embodiment of the invention includes a memory cell having a magnet layer coupled to a metal layer and read line. The metal layer is also coupled to write and sense lines. During a write operation charge current is supplied to the metal layer via the write line and induces spin current and a magnetic state within the magnet layer based on the spin Hall effect. During a read operation read current is supplied, via the read line, to the magnet layer and then the metal layer and induces another spin current, within the metal layer, that generates an electric field and voltage, based on inverse spin Hall effect, at a sense node coupled to the sense line. The voltage polarity is based on the aforementioned magnetic state. The memory operates with a low supply voltage to drive charge, read, and spin currents. Other embodiments are described herein.
摘要翻译：本发明的实施例包括具有耦合到金属层和读取线的磁体层的存储单元。金属层也耦合到写入和感测线。在写入操作期间，通过写入线将充电电流提供给金属层，并且基于旋转霍尔效应引起磁体层内的自旋电流和磁状态。在读取操作期间，读取电流通过读取线被提供给磁体层，然后被提供给金属层，并且在金属层内产生基于反旋转霍尔效应产生电场和电压的另一个自旋电流耦合到感测线的感测节点。电压极性基于上述磁状态。存储器以低电源电压运行，以驱动电荷，读数和自旋电流。本文描述了其它实施例。

10. 发明申请

US20190056885A1 LOW SYNCH DEDICATED ACCELERATOR WITH IN-MEMORY COMPUTATION CAPABILITY 审中-公开
公开(公告)号：US20190056885A1
公开(公告)日：2019-02-21
申请号：US16160482
申请日：2018-10-15
申请人： Amrita Mathuriya , Sasikanth Manipatruni , Victor Lee , Huseyin Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , IAN YOUNG , Abhishek Sharma
发明人： Amrita Mathuriya , Sasikanth Manipatruni , Victor Lee , Huseyin Sumbul , Gregory Chen , Raghavan Kumar , Phil Knag , Ram Krishnamurthy , IAN YOUNG , Abhishek Sharma
IPC分类号： G06F3/06 , G06F12/0802 , G06F12/1081 , G06N3/04
摘要： 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.

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