Western Digital Technologies, Inc.

Sandisk Technologies Inc.

Pi-Feng Chiu

Won Ho Choi

Martin Lueker-Boden

Wen Ma

Minghai Qin

Dejan Vucinic

Chao Sun

Gerrit Jan Hemink

Martin Lueker-boden

Exemplary methods and apparatus are disclosed that implement super-sparse image/video compression by storing image dictionary elements within a cross-bar resistive random access memory (ReRAM) array (or other suitable cross-bar NVM array). In illustrative examples, each column of the cross-bar ReRAM array stores the values for one dictionary element (such as one 4×4 dictionary element). Methods and apparatus are described for training (configuring) the cross-bar ReRAM array to generate and store the dictionary elements by sequentially applying patches from training images to the array using an unstructured Hebbian training procedure. Additionally, methods and apparatus are described for compressing an input image by applying patches from the input image to the ReRAM array to read out cross-bar column indices identifying the columns storing the various dictionary elements that best fit the image. This may be done in parallel using a set of ReRAM arrays.

Non-volatile cross-bar memory with on-chip processing circuitry

An innovative low-bit-width device may include a first digital-to-analog converter (DAC), a second DAC, a plurality of non-volatile memory (NVM) weight arrays, one or more analog-to-digital converters (ADCs), and a neural circuit. The first DAC is configured to convert a digital input signal into an analog input signal. The second DAC is configured to convert a digital previous hidden state (PHS) signal into an analog PHS signal. NVM weight arrays are configured to compute vector matrix multiplication (VMM) arrays based on the analog input signal and the analog PHS signal. The NVM weight arrays are coupled to the first DAC and the second DAC. The one or more ADCs are coupled to the plurality of NVM weight arrays and are configured to convert the VMM arrays into digital VMM values. The neural circuit is configured to process the digital VMM values into a new hidden state.

Hardware accelerated discretized neural network

An apparatus is disclosed having a parity buffer having a plurality of parity pages and one or more dies, each die having a plurality of layers in which data may be written. The apparatus also includes a storage controller configured to write a stripe of data across two or more layers of the one or more dies, the stripe having one or more data values and a parity value. When a first data value of the stripe is written, it is stored as a current value in a parity page of the parity buffer, the parity page corresponding to the stripe. For each subsequent data value that is written, an XOR operation is performed with the subsequent data value and the current value of the corresponding parity page and the result of the XOR operation is stored as the current value of the corresponding parity page.

3-dimensional NAND flash layer variation aware SSD raid

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.

Methods to tolerate programming and retention errors of crossbar memory arrays

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.

Reconfigurable input precision in-memory computing

An illustrative embodiment disclosed herein is an apparatus including a non-volatile memory cell and multi-bit input circuitry that simultaneously receives a plurality of bits, receives a supply voltage, converts the plurality of bits and the supply voltage into a multiply voltage, and applies the multiply voltage to the non-volatile memory cell. The non-volatile memory cell may pass a memory cell current in response to the multiply voltage. A magnitude of the multiply voltage may represent a multiplier. The memory cell current may represent a product of the multiplier and a multiplicand stored in the non-volatile memory cell.

Binary weighted voltage encoding scheme for supporting multi-bit input precision

Enhanced techniques and circuitry are presented herein for artificial neural networks. These artificial neural networks are formed from artificial synapses, which in the implementations herein comprise a memory arrays having non-volatile memory elements. In one implementation, an apparatus comprises a plurality of non-volatile memory arrays configured to store weight values for an artificial neural network. Each of the plurality of non-volatile memory arrays can be configured to receive data from a unified buffer shared among the plurality of non-volatile memory arrays, operate on the data, and shift at least portions of the data to another of the plurality of non-volatile memory arrays.

Shifting architecture for data reuse in a neural network

Use of a NAND array architecture to realize a binary neural network (BNN) allows for matrix multiplication and accumulation to be performed within the memory array. A unit synapse for storing a weight of a BNN is stored in a pair of series connected memory cells. A binary input is applied as a pattern of voltage values on a pair of word lines connected to the unit synapse to perform the multiplication of the input with the weight by determining whether or not the unit synapse conducts. The results of such multiplications are determined by a sense amplifier, with the results accumulated by a counter.

Realization of binary neural networks in NAND memory arrays

Exemplary methods and apparatus are disclosed that implement super-sparse image/video compression by storing image dictionary elements within a cross-bar resistive random access memory (ReRAM) array (or other suitable cross-bar NVM array). In illustrative examples, each column of the cross-bar ReRAM array stores the values for one dictionary element (such as one 4&#xd7;4 dictionary element). Methods and apparatus are described for training (configuring) the cross-bar ReRAM array to generate and store the dictionary elements by sequentially applying patches from training images to the array using an unstructured Hebbian training procedure. Additionally, methods and apparatus are described for compressing an input image by applying patches from the input image to the ReRAM array to read out cross-bar column indices identifying the columns storing the various dictionary elements that best fit the image. This may be done in parallel using a set of ReRAM arrays.

Super-sparse image compression using cross-bar non-volatile memory device

A memory array controller includes memory media scanning logic to sample a bit error rate of memory blocks of a first memory device. A data management logic may then move data from the first memory device to a second memory device if the bit error rate matches a threshold level. The threshold level is derived from a configurable data retention time parameter for the first memory device. The configurable data retention time parameter may be received from a user or determined utilizing various known machine learning techniques.

Retention-aware data tiering algorithm for hybrid storage arrays

An apparatus is disclosed having a parity buffer having a plurality of parity pages and one or more dies, each die having a plurality of layers in which data may be written. The apparatus also includes a storage controller configured to write a stripe of data across two or more layers of the one or more dies, the stripe having one or more data values and a parity value. When a first data value of the stripe is written, it is stored as a currant value in a parity page of the parity buffer, the parity page corresponding to the stripe. For each subsequent data value that is written, an XOR operation is performed with the subsequent data value and the current value of the corresponding parity page and the result of the XOR operation is stored as the current value of the corresponding parity page.

Use of a non-volatile memory array architecture to realize a neural network (BNN) allows for matrix multiplication and accumulation to be performed within the memory array. A unit synapse for storing a weight of a neural network is formed by a differential memory cell of two individual memory cells, such as a memory cells having a programmable resistance, each connected between a corresponding one of a word line pair and a shared bit line. An input is applied as a pattern of voltage values on word line pairs connected to the unit synapses to perform the multiplication of the input with the weight by determining a voltage level on the shared bit line. The results of such multiplications are determined by a sense amplifier, with the results accumulated by a summation circuit. The approach can be extended from binary weights to multi-bit weight values by use of multiple differential memory cells for a weight.

Configurable precision neural network with differential binary non-volatile memory cell structure

Use of a non-volatile memory array architecture to realize a neural network (BNN) allows for matrix multiplication and accumulation to be performed within the memory array. A unit synapse for storing a weight of a neural network is formed by a differential memory cell of two individual memory cells, such as a memory cells having a programmable resistance, each connected between a corresponding one of a word line pair and a shared bit line. An input is applied as a pattern of voltage values on word line pairs connected to the unit synapses to perform the multiplication of the input with the weight by determining a voltage level on the shared bit line. The results of such multiplications are determined by a sense amplifier, with the results accumulated by a summation circuit.

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Milpitas, CA, United States of America

Pi-Feng Chiu