Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 a a currant value in a parity page of the parity buffer, the pants 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Enhanced techniques and circuitry are presented herein for artificial neural networks. These artificial neural networks are formed from artificial neurons, which in the implementations herein comprise a memory array having non-volatile memory elements. Neural connections among the artificial neurons are formed by interconnect circuitry coupled to input control lines and output control lines of the memory array to subdivide the memory array into a plurality of layers of the artificial neural network. Control circuitry is configured to transmit a plurality of iterations of an input value on input control lines of a first layer of the artificial neural network for inference operations by at least one or more additional layers. The control circuitry is also configured to apply an averaging function across output values successively presented on output control lines of a last layer of the artificial neural network from each iteration of the input value.

Abstract: A testing and repairing apparatus of through silicon via (TSV) disposed between a first and a second chips is provided. First terminals of a first and a second switches are coupled to a first terminal of the TSV. First terminals of a third and a fourth switches are coupled to a second terminal of the TSV. A first terminal of a first resister is coupled to a first voltage. A first selector is coupled between second terminals of the second switch and the first resister. A second selector is coupled between a second terminal of the fourth switch and a second voltage. A first control circuit detects the second terminal of the second switch, and controls the first switch, the second switch and the first selector. A second control circuit controls the third switch, the fourth switch and the second selector.

Abstract: A circuit and a method for controlling the write timing of a non-volatile memory are provided. The method includes the following steps. First, a resistance state switching of at least one memory cell of the non-volatile memory executing a writing operation is monitored to output a control signal. The memory cell stores data states with different resistance states. A write timing is input to the memory cell through a timing control line. Next, the write timing is generated based on a clock signal and the control signal. The write timing is enabled at the beginning of a cycle of the clock signal, and is disabled when the memory cell finishes the resistance state switching.

Abstract: A non-volatile static random access memory (NVSRAM) cell including a static random access circuit, first storage device, a second storage device, and a switch unit is provided. The static random access circuit has a first terminal and a second terminal respectively having a first voltage and a second voltage. Stored data in the first storage device and the second storage device are determined by the first voltage and the second voltage. The first storage device and the second storage device respectively have a first connection terminal and a second connection terminal. The switch unit is respectively coupled to the second connection terminals of the first storage device and the second storage device, and is controlled by a switching signal of a switch line to conduct the first storage device and the second storage device to a same bit line or a same complementary bit line.

Abstract: A circuit and a method for controlling the write timing of a non-volatile memory are provided. The method includes the following steps. First, a resistance state switching of at least one memory cell of the non-volatile memory executing a writing operation is monitored to output a control signal. The memory cell stores data states with different resistance states. A write timing is input to the memory cell through a timing control line. Next, the write timing is generated based on a clock signal and the control signal. The write timing is enabled at the beginning of a cycle of the clock signal, and is disabled when the memory cell finishes the resistance state switching.

Abstract: A non-volatile random access memory (NV-RAM) and an operation method thereof are provided. The NV-RAM includes a latch unit, a switch, and a first to fourth non-volatile memory elements. First terminals of the first and the third non-volatile memory elements respectively couple to a first voltage and a second voltage. A second terminal of the first non-volatile memory element and a first terminal of the second non-volatile memory element are coupled to a first terminal of the latch unit. A second terminal of the third non-volatile memory element and a first terminal of the fourth non-volatile memory element are coupled to a second terminal of the latch unit. Second terminals of the second and the fourth non-volatile memory element are coupled to a first terminal of the switch. A second terminal of the switch is coupled to a third voltage.

Abstract: A testing and repairing apparatus of through silicon via (TSV) disposed between a first and a second chips is provided. First terminals of a first and a second switches are coupled to a first terminal of the TSV. First terminals of a third and a fourth switches are coupled to a second terminal of the TSV. A first terminal of a first resister is coupled to a first voltage. A first selector is coupled between second terminals of the second switch and the first resister. A second selector is coupled between a second terminal of the fourth switch and a second voltage. A first control circuit detects the second terminal of the second switch, and controls the first switch, the second switch and the first selector. A second control circuit controls the third switch, the fourth switch and the second selector.

Abstract: A non-volatile random access memory (NV-RAM) and an operation method thereof are provided. The NV-RAM includes a latch unit, a switch, and a first to fourth non-volatile memory elements. First terminals of the first and the third non-volatile memory elements respectively couple to a first voltage and a second voltage. A second terminal of the first non-volatile memory element and a first terminal of the second non-volatile memory element are coupled to a first terminal of the latch unit. A second terminal of the third non-volatile memory element and a first terminal of the fourth non-volatile memory element are coupled to a second terminal of the latch unit. Second terminals of the second and the fourth non-volatile memory element are coupled to a first terminal of the switch. A second terminal of the switch is coupled to a third voltage.