Abstract: An anti-icing honeycomb core composite manufactured by forming an electromagnetic wave absorption layer by using dielectric fiber, molding the electromagnetic wave absorption layer into a honeycomb core structure by using a molded part including a first base, a second base, and an inner block, hardening the honeycomb core structure, and removing the molded part. The molding step includes first stacking, on the first base including a plurality of grooves in which the inner blocks each having a hexagonal column shape are able to be seated, a plurality of the inner blocks and a plurality of the electromagnetic wave absorption layers as the honeycomb core structure so that the electromagnetic wave absorption layer is disposed between the plurality of inner blocks, and second stacking covering the inner blocks and the electromagnetic wave absorption layers stacked on the first base with the second base having the same shape as the first base.

Abstract: Non-volatile memory structures for performing compute in memory inferencing for neural networks are presented. To improve performance, both in terms of speed and energy consumption, weight matrices are replaced with their singular value decomposition (SVD) and use of a low rank approximations (LRAs). The decomposition matrices can be stored in a single array, with the resultant LRA matrices requiring fewer weight values to be stored. The reduced sizes of the LRA matrices allow for inferencing to be performed more quickly and with less power. In a high performance and energy efficiency mode, a reduced rank for the SVD matrices stored on a memory die is determined and used to increase performance and reduce power needed for an inferencing operation.

Abstract: Methods and apparatus are disclosed for implementing principal component analysis (PCA) within a non-volatile memory (NVM) die of solid state drive (SSD) to reduce the dimensionality of machine learning data before the data is transferred to other components of the SSD, such as to a data storage controller equipped with a machine learning engine. The machine learning data may include, for example, training images for training an image recognition system in which the SSD is installed. In some examples, the on-chip PCA components of the NVM die are configured as under-the-array or next-to-the-array components. In other examples, one or more arrays of the NVM die are configured as multiplication cores for performing PCA matrix multiplication. In still other aspects, multiple NVM dies are arranged in parallel, each with on-chip PCA components to permit parallel concurrent on-chip processing of machine learning data.

Abstract: Techniques are presented for performing in-memory matrix multiplication operations for binary input, binary weight valued convolution neural network (CNN) inferencing. The weights of a filter are stored in pairs of memory cells of a storage class memory device, such as a ReRAM or phase change memory based devices. To reduce current consumption, the binary valued filters are transformed into ternary valued filters by taking sums and differences of binary valued filter pairs. The zero valued weights of the transformed filters are stored as a pair of high resistance state memory cells, reducing current consumption during convolution. The results of the in-memory multiplications are pair-wise combined to compensate for the filter transformations. To compensate for zero valued weights, a zero weight register stores the number of zero weights along each bit line and is used to initialize counter values for accumulating the multiplication operations.

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 on a pair of word lines connected to the unit synapse to perform the multiplication of the input with the weight. The results of such multiplications are determined by a sense amplifier, with the results accumulated by a counter. The arrangement extends to ternary inputs to realize a ternary-binary network (TBN) by adding a circuit to detect 0 input values and adjust the accumulated count accordingly. The arrangement further extends to a ternary-ternary network (TTN) by allowing 0 weight values in a unit synapse, maintaining the number of 0 weights in a register, and adjusting the count accordingly.

Abstract: Disclosed is a method for automatically replacing high-pressure gas barrels, in which when loading a high-pressure gas barrel is loaded on the lift of a cabinet so as to supply gas to the wafer production line in the semiconductor fabrication FAB process facility, at the time point of replacement of the high-pressure gas barrel, a used high-pressure gas barrel is separated from a connector holder and then a new high-pressure gas barrel is connected to the connector holder.

Abstract: A method for automatically opening or closing a gas barrel valve, includes: loading and aligning a gas barrel in a cabinet; separating an end cap from the gas barrel; screw-coupling a connector holder to a gas spray nozzle, from which the ends cap has been removed; winding a spring around a first shaft by enabling forward rotation of the first shaft while suppressing reverse rotation of the first shaft, which is installed in a valve handle holder so as to idle; opening a valve by enabling reverse rotation of a valve handle of the gas barrel while preventing forward rotation of the valve handle holder; and automatically closing the valve at the time of replacement of the gas barrel or when a gas leak is detected.

Abstract: Methods and systems for testing memory systems are disclosed. A refresh rate for a test system including a number of memory devices may be controlled based on estimated power scenario of a memory system design. In response to performance of a number of refresh operations on the memory devices and based on the refresh rate, one or more conditions of the test system may be monitored to generate estimated performance data for the memory system design.

Abstract: Techniques are presented for accelerating in-memory matrix multiplication operations for a convolution neural network (CNN) inference in which the weights of a filter are stored in the memory of a storage class memory device, such as a ReRAM or phase change memory based device. To improve performance for inference operations when filters exhibit sparsity, a zero column index and a zero row index are introduced to account for columns and rows having all zero weight values. These indices can be saved in a register on the memory device and when performing a column/row oriented matrix multiplication, if the zero row/column index indicates that the column/row contains all zero weights, the access of the corresponding bit/word line is skipped as the result will be zero regardless of the input.

Abstract: Methods, systems, and devices for initializing memory systems are described. A memory system may transmit, to a host system over a first channel, signaling indicative of a first set of values for a set of parameters associated with communicating information over a second channel between a storage device of the memory system and a memory device of the memory system. The host system may transmit, to the memory system, additional signaling associated with the first set of values for the set of parameters. For instance, the host system may transmit a second set of values for the set of parameters, an acknowledgement to use the first set of values, or a command to perform a training operation on the second channel to identify a second set of values for the set of parameters. The memory system may communicate the information over the second channel based on the additional signaling.

Abstract: 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.

Abstract: 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.

Abstract: A non-volatile memory device includes an array of non-volatile memory cells that are configured to store weights of a neural network. Associated with the array is a data latch structure that includes a page buffer, which can store weights for a layer of the neural network that is read out of the array, and a transfer buffer, that can store inputs for the neural network. The memory device can perform multiply and accumulate operations between inputs and weight of the neural network within the latch structure, avoiding the need to transfer data out of the array and associated latch structure for portions of an inference operation. By using binary weights and inputs, multiplication can be performed by bit-wise XNOR operations. The results can then be summed and activation applied, all within the latch structure.

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: Disclosed is a high-pressure gas cylinder automatic replacement device, including: a holder fixedly provided on a main plate to be positioned at one side of a connector holder of a high-pressure gas cylinder connection unit; a movable member rotatably provided at the holder; a two-stage actuator provided at the holder such that rods thereof are respectively connected to the movable member and a bracket fixed to the main plate; a docking actuator enabling the movable member to move from the holder towards the connector holder side or vice versa; a gasket removal cartridge provided at the movable member to automatically remove used gaskets from the connector holder and to accommodate the same in turn; and a gasket insertion cartridge provided at the movable member positioned at the upper portion of the gasket removal cartridge to insert a new gasket accommodated therein into the connector holder.

Abstract: Apparatuses, systems, and methods are disclosed for magnetoresistive random access memory. A magnetic tunnel junction (MTJ) for storing data may include a reference layer. A free layer of an MTJ may be separated from a reference layer by a barrier layer. A free layer may be configured such that one or more resistance states for an MTJ correspond to one or more positions of a magnetic domain wall within the free layer. A domain stabilization layer may be coupled to a portion of a free layer, and may be configured to prevent migration of a domain wall into the portion of the free layer.

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: An apparatus performs vector matrix multiplication (VMM) for an analog neural network (ANN). The apparatus includes a column of NAND flash cells in series, where each NAND flash cell includes a control gate; a bit line connected to the column of NAND flash cells, where a current drawn from the NAND flash cells flows to the bit line; an integrator connected to the bit line; and a controller having programmed instructions to control the column of NAND flash cells by setting the voltage of the control gate of each NAND flash cell.

Abstract: An apparatus is provided that includes a memory device including a plurality of sub-arrays, and a memory controller. The memory controller is configured to determine a value of a parameter of a corresponding write pulse for each bit of a word based on a relative importance of each bit, and write each bit of the word to a corresponding one of the plurality of sub-arrays using the corresponding write pulses.

Abstract: Disclosed is a device for automatically opening or closing a gas barrel valve. The device includes: a main plate installed so as to move up and down and to align the position of a gas barrel loaded in a cabinet; a gas barrel connecting portion installed on the lower portion of the main plate, separating an end cap from the gas barrel and storing the end cap, and then automatically screw-coupling a connector holder to a gas spray nozzle; a valve handle unit installed on the main plate so as to rotate around a first shaft and rotating a valve handle of the gas barrel such that the valve handle is locked or unlocked, while encompassing the valve handle of the gas barrel; and a valve handle opening or closing unit installed on the upper portion of the main plate so as to operate the valve handle unit in a direction, in which a valve of the gas barrel is opened.