G11C11/54

Pulsing synaptic devices based on phase-change memory to increase the linearity in weight update

According to one embodiment, a method, computer system, and computer program product for increasing linearity of a weight update of a phase change memory (PCM) cell is provided. The present invention may include applying a RESET pulse to amorphize the phase change material of the PCM cell; responsive to applying the RESET pulse, applying an incubation pulse to the PCM cell; and applying a plurality of partial SET pulses to incrementally increase the conductance of the PCM cell.

Pulsing synaptic devices based on phase-change memory to increase the linearity in weight update

According to one embodiment, a method, computer system, and computer program product for increasing linearity of a weight update of a phase change memory (PCM) cell is provided. The present invention may include applying a RESET pulse to amorphize the phase change material of the PCM cell; responsive to applying the RESET pulse, applying an incubation pulse to the PCM cell; and applying a plurality of partial SET pulses to incrementally increase the conductance of the PCM cell.

Methods to tolerate programming and retention errors of crossbar memory arrays

Systems and methods for reducing the impact of defects within a crossbar memory array when performing multiplication operations in which multiple control lines are concurrently selected are described. A group of memory cells within the crossbar memory array may be controlled by a local word line that is controlled by a local word line gating unit that may be configured to prevent the local word line from being biased to a selected word line voltage during an operation; the local word line may instead be set to a disabling voltage during the operation such that the memory cell currents through the group of memory cells are eliminated. If a defect has caused a short within one of the memory cells of the group of memory cells, then the local word line gating unit may be programmed to hold the local word line at the disabling voltage during multiplication operations.

Reconfigurable input precision in-memory computing

Technology for reconfigurable input precision in-memory computing is disclosed herein. Reconfigurable input precision allows the bit resolution of input data to be changed to meet the requirements of in-memory computing operations. Voltage sources (that may include DACs) provide voltages that represent input data to memory cell nodes. The resolution of the voltage sources may be reconfigured to change the precision of the input data. In one parallel mode, the number of DACs in a DAC node is used to configure the resolution. In one serial mode, the number of cycles over which a DAC provides voltages is used to configure the resolution. The memory system may include relatively low resolution voltage sources, which avoids the need to have complex high resolution voltage sources (e.g., high resolution DACs). Lower resolution voltage sources can take up less area and/or use less power than higher resolution voltage sources.

Method for combining analog neural net with FPGA routing in a monolithic integrated circuit

A method for implementing a neural network system in an integrated circuit includes presenting digital pulses to word line inputs of a matrix vector multiplier including a plurality of word lines, the word lines forming intersections with a plurality of summing bit lines, a programmable Vt transistor at each intersection having a gate connected to the intersecting word line, a source connected to a fixed potential and a drain connected to the intersecting summing bit line, each digital pulse having a pulse width proportional to an analog quantity. During a charge collection time frame charge collected on each of the summing bit lines from current flowing in the programmable Vt transistor is summed. During a pulse generating time frame digital pulses are generated having pulse widths proportional to the amount of charge that was collected on each summing bit line during the charge collection time frame.

Power efficient near memory analog multiply-and-accumulate (MAC)
11574173 · 2023-02-07 · ·

A near memory system is provided for the calculation of a layer in a machine learning application. The near memory system includes an array of memory cells for storing an array of filter weights. A multiply-and-accumulate circuit couples to columns of the array to form the calculation of the layer.

ELEMENTS FOR IN-MEMORY COMPUTE

A memory array arranged in multiple columns and rows. Computation circuits that each calculate a computation value from cell values in a corresponding column. A column multiplexer cycles through multiple data lines that each corresponds to a computation circuit. Cluster cycle management circuitry determines a number of multiplexer cycles based on a number of columns storing data of a compute cluster. A sensing circuit obtains the computation values from the computation circuits via the column multiplexer as the column multiplexer cycles through the data lines. The sensing circuit combines the obtained computation values over the determined number of multiplexer cycles. A first clock may initiate the multiplexer to cycle through its data lines for the determined number of multiplexer cycles, and a second clock may initiate each individual cycle. The multiplexer or additional circuitry may be utilized to modify the order in which data is written to the columns.

Redundant memory access for rows or columns containing faulty memory cells in analog neural memory in deep learning artificial neural network

Numerous embodiments are disclosed for accessing redundant non-volatile memory cells in place of one or more rows or columns containing one or more faulty non-volatile memory cells during a program, erase, read, or neural read operation in an analog neural memory system used in a deep learning artificial neural network.

System and method for classifying data using neural networks with errors

A computing device includes one or more processors, random access memory (RAM), and a non-transitory computer-readable storage medium storing instructions for execution by the one or more processors. The computing device receives first data and classifies the first data using a neural network that includes at least one quantized layer. The classifying includes reading values from the random access memory for a set of weights of the at least one quantized layer of the neural network using first read parameters corresponding to a first error rate.

Trim level adjustments for memory based on data use

A method includes determining a quantity of refresh operations performed on a block of a memory device of a memory sub-system and determining a quantity of write operations and a quantity of read operations performed to the block. The method also includes determining the block is read dominant using the quantity of write operations and the quantity of read operations and determining whether the quantity of refresh operations has met a criteria. The method further includes, responsive to determining that the block is read dominant and that the quantity of refresh operations has met the criteria, modifying trim settings used to operate the block of the memory device.