Abstract: According to an example, a hybrid synaptic architecture based neural network may be implemented by determining, from input data, information that is to be recognized, mined, and/or synthesized by a plurality of analog neural cores. Further, the hybrid synaptic architecture based neural network may be implemented by determining, based on the information, selected ones of the plurality of analog neural cores that are to be actuated to identify a data subset of the input data to generate, based on the analysis of the data subset, results of the recognition, mining, and/or synthesizing of the information.

Abstract: An example method involves receiving, at a first memory node, data to be written at a memory location in the first memory node. The data is received from a device. At the first memory node, old data is read from the memory location, without sending the old data to the device. The data is written to the memory location. The data and the old data are sent from the first memory node to a second memory node to store parity information in the second memory node without the device determining the parity information. The parity information is based on the data stored in the first memory node.

Abstract: Examples disclosed herein relate to a circuit having first and second analog processors and an analog-to-digital converter coupled to the first and second analog processors. The first analog processor provides a first analog signal having a voltage representing a function of a first vector and a second vector. The second analog processor provides a second analog signal having a voltage representing a function of a binary inverse of the first vector and the second vector. The analog-to-digital converter receives the first analog signal and the second analog signal, compares a signal selected from a group consisting of the first analog signal and the second analog signal to a reference voltage and based on the comparison to the reference voltage, determines a digital result representing the function of the first vector and the second vector.

Abstract: A circuit is provided. In an example, the circuit includes a memory array that includes a plurality of memory cells to store a matrix and a plurality of data lines coupled to the plurality of memory cells to provide a first set of values of the matrix. The circuit includes a multiplier coupled to the plurality of data lines to multiply the first set of values by a second set of values to produce a third set of values. A summing unit is included that is coupled to the multiplier to sum the third set of values to produce a sum. The circuit includes a shifting unit coupled to the summing unit to shift the sum and to add the shifted sum to a running total.

Abstract: A method to access two memory cells include determining a first cell current flowing through a first memory cell by subtracting a sneak current associated with the first memory cell from a first access current of the first bitline and determining a second cell current flowing through a second memory cell in the first bitline or a second bitline by subtracting the sneak current associated with the first memory cell from a second access current of the first bitline or the second bitline.

Abstract: Examples herein relate to convolution accelerators. An example convolution accelerator may include a transformation crossbar array programmed to calculate a Fourier Transformation of a first vector with a transformation matrix and a Fourier Transformation of a second vector with the transformation matrix. A circuit of the example convolution accelerator may multiply the Fourier Transformation of the first vector with the Fourier Transformation of the second vector to calculate a product vector. The example convolution accelerator may have an inverse transformation crossbar array programmed to calculate an Inverse Fourier Transformation of the product vector according to an inverse transformation matrix.

Abstract: Local checkpointing using a multi-level call is described herein. An example method includes storing a first datum in a first level of a multi-level cell. A second datum is stored in a second level of the multi-level cell, the second datum representing a checkpoint of the first datum. The first datum is copied from the first level to the second level of the multi-level cell to create the checkpoint.

Abstract: An example method involves receiving, at a first memory node, data to be written at a memory location in the first memory node. The data is received from a device. At the first memory node, old data is read from the memory location, without sending the old data to the device. The data is written to the memory location. The data and the old data are sent from the first memory node to a second memory node to store parity information in the second memory node without the device determining the parity information. The parity information is based on the data stored in the first memory node.

Abstract: Hierarchical computation on sparse matrix rows is disclosed. One example is a circuit including a sparse row processor to identify a sparse row of a matrix, where the identified row has a number of non-zero entries less than a threshold, associate a sub-vector of an input vector with a sub-row of the identified row, where the sub-row comprises the non-zero entries of the identified row, and where entries in the sub-vector correspond to the non-zero entries in the identified row in a multiplication operation, and map entries in the matrix to an engine formed from a memristor array. A stream buffer queues sub-vectors based on a position of associated sub-rows of identified sparse rows. The engine computes analog multiplication results between sub-rows and their associated sub-vectors, where each column of the array is configured to hierarchically compute multiple multiplication results based on the queue.

Abstract: This disclosure provides a circuit that includes a ramp generator to apply a voltage ramp to a resistive memory cell. A sensing circuit can enable the ramp generator and monitor a current output received from the resistive memory cell in response to the applied voltage ramp, wherein the sensing circuit compares the current output to a predetermined current threshold to determine the state of the resistive memory cell.

Abstract: In an example, a method includes receiving, in a memory, input data to be processed in a first and a second processing layer. A processing operation of the second layer may be carried out on an output of a processing operation of the first processing layer. The method may further include assigning the input data to be processed according to at least one processing operation of the first layer, which may comprise using a resistive memory array, and buffering output data. It may be determined whether the buffered output data exceeds a threshold data amount to carry out at least one processing operation of the second layer and when it is determined that the buffered output data exceeds the threshold data amount, at least a portion of the buffered output data may be assigned to be processed according to a processing operation of the second layer.

Abstract: A detector detects, using an error code, an error in data stored in a memory. The detector determines whether the error is uncorrectable using the error code. In response to determining that the error is uncorrectable, an error handler associated with an application is invoked to handle the error in the data by recovering the data to an application-wide consistent state.

Abstract: Examples herein relate to convolution accelerators. An example convolution accelerator may include a transformation crossbar array programmed to calculate a Fourier Transformation of a first vector with a transformation matrix and a Fourier Transformation of a second vector with the transformation matrix. A circuit of the example convolution accelerator may multiply the Fourier Transformation of the first vector with the Fourier Transformation of the second vector to calculate a product vector. The example convolution accelerator may have an inverse transformation crossbar array programmed to calculate an Inverse Fourier Transformation of the product vector according to an inverse transformation matrix.

Abstract: According to an example, in a method for determining a resistance state of a cell in a crossbar memory array, a first read voltage may be applied across a cell to sense a first cell current. In addition, a second read voltage may be applied across the cell to sense a second cell current. A difference value between the first cell current and the second cell current may be identified and a resistance state of the cell may be determined based on the difference value.

Abstract: Examples herein relate to circuits for computing vector-matrix multiplications involving negative values. A first memory crossbar array may be mapped to a first matrix which includes the positive values of an input matrix. A second memory crossbar array may be mapped to a second matrix which includes the negative values of the input matrix. An analog-to-digital converter may generate digital intermediate multiplication results based on analog results computed by the memory crossbar arrays. The digital intermediate multiplication results may include an intermediate result corresponding to a multiplication of each of the first vector and second vector with each of the first matrix and the second matrix. A controller may aggregate the digital intermediate results to generate a combined multiple result that represents the vector-matrix multiplication of the input vector and the input matrix.

Abstract: A method that includes evaluating, with a controller, local error detection (LED) information in response to a first memory access operation is disclosed. The LED information is evaluated per cache line segment of data associated with a rank of a memory. The method further includes determining an error in at least one of the cache line segments based on an error detection code and determining whether global error correction (GEC) data for a first cache line associated with the at least one cache line segment is stored in a GEC cache in the controller. The method also includes correcting the first cache line associated with the at least one cache line segment based on the GEC data retrieved from the GEC cache in the controller without accessing GEC data from a memory.

Abstract: Examples herein relate to circuits for computing vector-matrix multiplications involving negative values. A first memory crossbar array may be mapped to a first matrix which includes the positive values of an input matrix. A second memory crossbar array may be mapped to a second matrix which includes the negative values of the input matrix. An analog-to-digital converter may generate digital intermediate multiplication results based on analog results computed by the memory crossbar arrays. The digital intermediate multiplication results may include an intermediate result corresponding to a multiplication of each of the first vector and second vector with each of the first matrix and the second matrix. A controller may aggregate the digital intermediate results to generate a combined multiple result that represents the vector-matrix multiplication of the input vector and the input matrix.

Abstract: A non-volatile multi-level cell (“MLC”) memory device is disclosed. The memory device has an array of non-volatile memory cells, an array of non-volatile memory cells, with each non-volatile memory cell storing multiple groups of bits. A row buffer in the memory device has multiple buffer portions, each buffer portion storing one or more bits from the memory cells and having different read and write latencies and energies.

Abstract: A disclosed example apparatus includes a row address register (412) to store a row address corresponding to a row (608) in a memory array (602). The example apparatus also includes a row decoder (604) coupled to the row address register to assert a signal on a wordline (704) of the row after the memory receives a column address. In addition, the example apparatus includes a column decoder (606) to selectively activate a portion of the row based on the column address and the signal asserted on the wordline.

Abstract: An example device in accordance with an aspect of the present disclosure includes a characterization engine and an allocation engine. The characterization engine is to receive information and characterizes expected temperature exposure of a memory device, to store a characterization profile of a plurality of memory devices of a computing system. The characterization engine is to refer to the characterization profile to identify the expected temperature exposure for a given memory device.