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: Apparatuses, systems, and methods are disclosed for magnetoresistive random access memory. A magnetic tunnel junction for storing data may include a reference layer, a barrier layer, and a free layer. A barrier layer may be disposed between a reference layer and a free layer. A free layer may include a nucleation region and an arm. A nucleation region may be configured to form a magnetic domain wall. An arm may be narrower than a nucleation region and may extend from the nucleation region. An arm may include a plurality of pinning sites formed at predetermined locations along the arm for pinning a domain wall.

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: 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: 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: 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: A pipe support device for a transformer is proposed. A brace having a grid shape and serving as a reinforcing member is provided on the surface of an outer housing constituting the exterior of the transformer. Supports are installed at predetermined intervals on the brace to be orthogonal thereto. A support base is positioned on each of the supports, and a pipe holder is coupled to the support base to support a pipe. An elastic supporting pad is positioned between the support base and the pipe and opposite flange portions of the pipe holder are seated on and coupled to the elastic supporting pad. An elastic close-contact pad is positioned between the pipe and an arched portion of the pipe holder and is brought into close contact with the pipe.

Abstract: A system and method for refreshing memory cells of a memory device includes storing each bit of a B-bit word in a different sub-array of a memory device. Each of the bits is associated with a bit position, and the memory device includes a plurality of sub-arrays. The system and method also include determining a refresh interval for a plurality of the bit positions based upon a relative importance of the plurality of the bit positions to a performance of a machine learning or signal processing task involving the B-bit word. The refresh interval is based upon a fidelity metric and a resource metric. The system and method further include refreshing the plurality of sub-arrays based upon the refresh interval determined for the plurality of bit positions, and dynamically updating the refresh interval for the plurality of bit positions upon receiving a new fidelity metric or a new resource metric.

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: 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. The arrangement can be extended 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.

Abstract: Neuron circuit structures are presented which employ magnetic tunnel junction (MTJ) elements that change state probabilistically in response to application of electrical source currents that emulate synaptic activity. Some implementations form probabilistic neuron circuits using homogeneous perpendicular spin-transfer torque (STT) MTJ elements. These neuron circuits include a perpendicular STT reference MTJ element coupled via an electrical node with a perpendicular STT neuron MTJ element that can change state. The electrical node for each neuron circuit couples a neuron MTJ element or “perturbation” element to a reference element, and also to an electrical current employed to influence probabilistic magnetization state changes in the perturbation MTJ element. A read current can be applied to the perturbation element to produce an output voltage at the electrical node indicative of a magnetization state of the perturbation element.

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: A gas circuit breaker of a gas-insulated switchgear is proposed. The gas circuit breaker includes a driving part (10) that operates when fault current occurs, and a fixed part (20) that is coupled to and separated from the driving part (10), thereby establishing electric connection. The driving part (10) includes a first main electrode (12) and a first arc electrode (14). The fixed part (20) includes a second main electrode (22) that is coupled to and separated from the first main electrode (12). The fixed part (20) also includes a second arc electrode (24). The first arc electrode (14) and the second arc electrode (24) are separated after separation of the first and second main electrodes (12, 22), thereby generating an arc. A first activation lever (30) is connected to a first connection link (19). A second activation lever (40) is connected to the first activation lever (30).

Abstract: True random number generation (TRNG) circuits are presented which employ magnetic tunnel junction (MTJ) elements that can change magnetization state probabilistically in response to application of electrical pulses. Some implementations include pulse generators which apply perturbation sequences to the MTJ elements. The MTJ elements responsively produce randomized outputs related to changes in magnetization states. Probability compensators are included which monitor for deviations in measured probabilities in the randomized outputs from a target probability. The probability compensators make adjustments to the perturbation sequences to influence probabilistic changes in the magnetization states of the MTJ elements and bring the measured probabilities to within a predetermined deviation from the target probability.

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: Ternary content addressable memory (TCAM) circuits are provided herein. In one example implementation, a TCAM circuit can include a first spin-orbit torque (SOT) magnetic tunnel junction (MTJ) element having a pinned layer coupled to a first read transistor controlled by a first search line, and having a spin hall effect (SHE) layer coupled in a first configuration across complemented write inputs. The TCAM circuit can include a second SOT MTJ element having a pinned layer coupled to a second read transistor controlled by a second search line, and having a SHE layer coupled in a second configuration across the complemented write inputs. The TCAM circuit can include a bias transistor configured to provide a bias voltage to drain terminals of the first read transistor and the second read transistor, and a voltage keeper element that couples the drain terminals to a match indicator line.