Arm Limited

Fernando Garcia Redondo

Shidhartha Das

Mudit Bhargava

Pranay Prabhat

Glen Arnold Rosendale

Parameshwarappa Anand Kumar Savanth

Supreet Jeloka

Sahan Sajeewa Hiniduma Udugama Gamage

Jonas Švedas

James Edward Myers

Matthew Mattina

Paul Nicholas Whatmough

Gary Dale Carpenter

George McNeil Lattimore

Mbou Eyole

Matthew James Walker

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

A method comprising, in response to a power-drop warning, beginning a checkpointing process comprising storing, to a non-volatile memory, execution state associated with data processing operations performed by data processing circuitry. The method also comprises maintaining, in the non-volatile memory, a checkpoint-progress indication to indicate which of multiple sections of the execution state have been stored as part of the checkpointing process.

Checkpoint-progress status

In response to a power-loss warning event occurring during data processing, a checkpointing process is performed to save a checkpoint of context data associated with the data processing to non-volatile data storage. In response to detection of a power recovery event occurring when the checkpointing process is still in progress, it is determined whether a checkpoint abort condition is satisfied, based at least on a checkpoint progress indication indicative of progress of the checkpointing process. If the checkpoint abort condition is unsatisfied, the checkpointing process can continue. If the checkpoint abort condition is satisfied, the checkpointing process is aborted to allow the data processing to resume.

Checkpointing

Briefly, embodiments, such as methods and/or systems for employing memory devices.

System, method and/device for managing memory devices

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

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

A method and apparatus for performing refactored multiply-and-accumulate operations is provided. A summing array includes a plurality of non-volatile memory elements arranged in columns. Each non-volatile memory element in the summing array is programmed to a high resistance state or a low resistance state based on weights of a neural network. The summing array is configured to generate a summed signal for each column based, at least in part, on a plurality of input signals. A multiplying array is coupled to the summing array, and includes a plurality of non-volatile memory elements. Each non-volatile memory element in the multiplying array is programmed to a different conductance level based on the weights of the neural network. The multiplying array is configured to generate an output signal based, at least in part, on the summed signals from the summing array.

Refactoring mac operations

A non-volatile memory (NVM) crossbar for an artificial neural network (ANN) accelerator is provided. The NVM crossbar includes row signal lines configured to receive input analog voltage signals, multiply-and-accumulate (MAC) column signal lines, a correction column signal line, a MAC cell disposed at each row signal line and MAC column signal line intersection, and a correction cell disposed at each row signal line and correction column signal line intersection. Each MAC cell includes one or more programmable NVM elements programmed to an ANN unipolar weight, and each correction cell includes one or more programmable NVM elements. Each MAC column signal line generates a MAC signal based on the input analog voltage signals and the respective MAC cells, and the correction column signal line generates a correction signal based on the input analog voltage signals and the correction cells. Each MAC signal is corrected based on the correction signal.

Non-volatile memory accelerator for artificial neural networks

A multiply-accumulate method and architecture are disclosed. The architecture includes a plurality of networks of non-volatile memory elements arranged in tiled columns. Logic digitally modulates the equivalent conductance of individual networks among the plurality of networks to map the equivalent conductance of each individual network to a single weight within the neural network. A first partial selection of weights within the neural network is mapped into the equivalent conductances of the networks in the columns to enable the computation of multiply-and-accumulate operations by mixed-signal computation. The logic updates the mappings to select a second partial selection of weights to compute additional multiply-and-accumulate operations and repeats the mapping and computation operations until all computations for the neural network are completed.

Non-volatile memory-based compact mixed-signal multiply-accumulate engine

Subject matter disclosed herein may relate to wireless energy harvesting devices and may relate more particularly to system-on-a-chip testing for wireless energy harvesting devices.

System-on-a-chip testing for energy harvesting devices

According to one implementation of the present disclosure, a method for power management is disclosed. The method includes: computing, by a central processing unit, software instructions of a software workload in an active-mode operation corresponding to a first operating point on a performance curve of a performance mode; transitioning from instances of the active-mode operation to instances of standby-mode operation of the CPU, and recording, by a time tracking element, each of a plurality of standby entry data points; transitioning from the instances of the standby-mode operation to the instances of the active-mode operation of the CPU, and recording, by the time tracking element, each of a plurality of standby exit data points; and determining a second operating point on the performance curve of the performance mode based on the recorded standby entry data points and the recorded standby exit data points.

Systems and methods of power management

An integrated circuit includes a primary memory array with cells switchable between first and second states. The circuit also includes sacrificial memory cells; each fabricated to be switchable between the first and second states and associated with at least one row of the primary array. A controller is configured to detect a write operation to a row of the primary array, stress a sacrificial cell associated with the row and detect a failure of the associated sacrificial cell. The sacrificial cells are fabricated to have lower write-cycle endurance than cells of the primary array or are subjected to more stress. Failure of a row of the primary array is predicted based, at least in part, on a detected failure of the associated sacrificial cell.

Memory device with on-chip sacrificial memory cells

Various implementations are related to an apparatus with memory cells arranged in columns and rows, and the memory cells are accessible with a column control voltage for accessing the memory cells via the columns and a row control voltage for accessing the memory cells via the rows. The apparatus may include neural network circuitry having neuronal junctions that are configured to receive, record, and provide information related to incoming voltage spikes associated with input signals based on resistance through the neuronal junctions. The apparatus may include stochastic re-programmer circuitry that receives the incoming voltage spikes, receives the information provided by the neuronal junctions, and reconfigure the information recorded in the neuronal junctions based on the incoming voltage spikes associated with the input signals along with a programming control signal provided by the memory circuitry.

Neural network architecture

In a particular implementation, a method includes: providing a first voltage to a word-line coupled to a first transistor device; providing a second voltage to a bit-line coupled to the first transistor device; providing a third voltage to a source-line coupled between a programmable resistive device and a voltage control element. Also, the first transistor device is coupled to the programmable resistive device and the voltage control element, where the programmable resistive device is configured to replace a first data value by writing a second data value in the programmable resistive device. Moreover, in response to a voltage difference across the programmable resistive device exceeding a particular threshold, limiting the voltage difference by one of reducing the second voltage on the bit-line or increasing the third voltage on the source-line.

Growing community of inventors

Cambridge, United Kingdom

Fernando Garcia Redondo