Arm Limited

Carnegie Mellon University

Mudit Bhargava

Shidhartha Das

Supreet Jeloka

Joel Thornton Irby

Andy Wangkun Chen

Glen Arnold Rosendale

Saurabh Pijuskumar Sinha

Rahul Mathur

Brian Tracy Cline

Akhilesh Ramlaut Jaiswal

George McNeil Lattimore

Vikas Chandra

Akshay Kumar

Pranay Prabhat

Piyush Agarwal

Fernando Garcia Redondo

Yew Keong Chong

Paul Nicholas Whatmough

Xiaoqing Xu

Bal S Sandhu

Gregory Munson Yeric

Zhi-Gang Liu

Vinay Vashishtha

Kenneth Wei-An Mai

Lucian Shifren

Gus Wai-Yan Yeung

Sriram Thyagarajan

Paul Gilbert Meyer

Fakhruddin Ali Bohra

Wendy Arnott Elsasser

James Dennis Dodrill

Carl Wayne Vineyard

Joshua Randall

Alan Jeremy Becker

Stephen Lewis Moore

Shruti Aggarwal

James Edwards Myers

Fernando García Redondo

Teyuh Alice Chou

A compute-in-memory (CIM) array module and a method for performing dynamic saturation detection for a CIM array are provided. The CIM array module includes a CIM array, saturation detection units (SDUs) and a controller. The CIM array includes selectable row signal lines, column signal lines and cells. Each cell is located at an intersection of a selectable row signal line and a column signal line, and each cell has a programmable conductance. The SDUs are selectively coupled to at least one column signal line, and each SDU is configured to, for each column signal line, generate an analog signal, and identify the column signal line as a saturated column signal line when a voltage of the analog signal is greater than a saturation threshold voltage, or a current of the analog signal is greater than a saturation threshold current.

Memory for artificial neural network accelerator

Various implementations described herein are related to a device having bitline drivers coupled to passgates of bitcells via bitlines and buried metal lines formed within a substrate including a buried enable signal line and a buried ground line coupled to ground connections of the bitline drivers. The buried enable signal line transfers a negative bias to a selected bitline of the bitlines via the buried ground line that is coupled to the ground connections of the bitline drivers so as to increase gate-source bias of the passgates of the selected bitcell to thereby enhance write capability of the selected bitcell.

Buried metal techniques

Disclosed are devices and techniques for facilitating transmission of light signals between optical waveguides formed on integrated circuit (IC) devices. In an implementation, one or more first waveguides may be formed in a structure such that at least a portion of the one or more first waveguides are exposed for optical connectivity. The structure may comprise first features to enable the structure to be interlocked with an IC device comprising second features complementary with the first features, so as to align at least a portion of the one or more first waveguides exposed to optically couple with one or more second waveguides formed in the first integrated circuit device.

Optical waveguide connecting device

According to one implementation of the present disclosure, a method includes performing a spatial alignment of at least one of first or second data tiers of a circuit; and performing a computation based on the spatial alignment of the at least one of the first and second data tiers. According to another implementation of the present disclosure, a circuit includes: a compute circuitry; and at least first and second data tiers of two or more data tiers positioned at least partially overlapping one another. In an example, each of the at least first and second data tiers is coupled to the compute circuitry. In certain implementations, the positioning of the first and second data tiers at least partially overlapping one another corresponds to a spatial alignment.

Methods and circuits of spatial alignment

Various implementations described herein are related to a method. The method may apply a write control voltage to a bitcell. The method may gradually ramp the write control voltage to the bitcell. The method may terminate application of the write control voltage to the bitcell when a write operation is sensed in the bitcell.

Ramp write techniques

A memory for an artificial neural network (ANN) accelerator is provided. The memory includes a first bank, a second bank and a bank selector. Each bank includes at least two word lines and a plurality of write word selectors. Each word line stores a plurality of words, and each word has a plurality of bytes. Each write word selector has an input port and a plurality of output ports, is coupled to a corresponding word in each word line, and is configured to select a byte of the corresponding word of a selected word line based on a byte select signal. The bank selector is coupled to the write word selectors of the first bank and the second bank, and configured to select a combination of write word selectors from at least one of the first bank and the second bank based on a bank select signal.

Memory for an artificial neural network accelerator

According to one implementation of the present disclosure, a circuit comprises: a memory array comprising one or more groupings of bitcells, one or more bitlines, and one or more wordlines; and one or more canary circuits coupled to the memory array, wherein each of the canary circuits is configured to predict at least partial breakdown of a corresponding grouping of bitcells in the memory array. According to one implementation of the present disclosure, a method includes: providing an excitation stress on one or more canary circuits corresponding to a grouping of bitcells in a memory array; detecting at least a partial breakdown of the one or more canary circuits; and generating a flag.

Circuits and methods of detecting at least partial breakdown of canary circuits

Briefly, embodiments, such as methods and/or systems for operations and/or procedures to test magnetic memory devices. In a particular implementation, a bit error rate of a magnetic memory device may be estimated based, at least in part, on an observed bit error rate in the presence of an externally applied magnetic field.

System, method and/or apparatus for magnetic memory testing

Disclosed are methods, systems and devices for operation of memory device. In one aspect, volatile memory bitcells and non-volatile memory bitcells may be integrated to facilitate transfer of stored values between the volatile and non-volatile memory bitcells.

Method, system and device for integration of volatile and non-volatile memory bitcells

Various implementations described herein refer to a method for tracking abnormal incidents while monitoring activity of logic circuitry. The method may include detecting a tamper event related to the abnormal incidents and storing an attack signature related to the tamper event. The attack signature may be stored in non-volatile memory (NVM), such as, e.g., correlated electron random access memory (CeRAM).

Tamper detection and response techniques

Various implementations described herein are related to a device having voltage regulation architecture with multiple layers arranged in a multi-layer structure. The device may include one or more layers of the multiple layers with voltage regulation circuitry that may be configured to manage at least one of process variation and temperature variation between the multiple layers of the multi-layer structure.

Multi-tier memory architecture

Various implementations described herein are directed to a device having a multi-tiered memory structure with a first tier and a second tier arranged vertically in a stacked configuration. The device may have multiple transistors disposed in the multi-tiered memory structure with first transistors disposed in the first tier and second transistors disposed in the second tier. The device may have a single interconnect that vertically couples the first transistors in the first tier to the second transistors in the second tier.

According to one implementation of the present disclosure, an integrated circuit includes a memory macro unit, and one or more through silicon vias (TSVs) at least partially coupled through the memory macro unit. According to one implementation of the present disclosure, a computer-readable storage medium comprising instructions that, when executed by a processor, cause the processor to perform operations including: receiving a user input corresponding to dimensions of respective pitches of one or more through silicon vias (TSVs); determining whether dimensions of a memory macro unit is greater than a size threshold, wherein the size threshold corresponds to the received user input; and determining one or more through silicon via (TSV) positionings based on the determined dimensions of the memory macro unit.

TSV coupled integrated circuits and methods

According to one implementation of the present disclosure, an integrated circuit comprises a memory macro unit that includes an input/output (I/O) circuit block, where read/write circuitry of the I/O circuit block is apportioned on at least first and second tiers of the memory macro unit. In a particular implementation, read circuitry of the read/write circuitry is arranged on the first tier and write circuitry of the read/write circuitry is arranged on the second tier.

Circuitry apportioning of an integrated circuit

A memory for an artificial neural network (ANN) accelerator is provided. The memory includes a first bank, a second bank and a bank selector. Each bank includes at least two word lines and a plurality of read word selectors. Each word line stores a plurality of words, and each word has a plurality of bytes. Each read word selector has a plurality of input ports and an output port, is coupled to a corresponding word in each word line, and is configured to select a byte of the corresponding word of a selected word line based on a byte select signal. The bank selector is coupled to the read word selectors of the first bank and the second bank, and configured to select a combination of read word selectors from at least one of the first bank and the second bank based on a bank select signal.

Growing community of inventors

Austin, TX, United States of America

Mudit Bhargava