Abstract: A method, comprising: providing an electrical energy source having a specified amount of electrical energy; connecting an array comprising n magnetic tunnel junctions (MTJ) in parallel to said electrical energy source, wherein each of said MTJs is at a high resistance initial state; discharging said specified energy amount through said MTJs, thereby causing a random subset of said MTJs to switch to a lower resistance state; determining a post-discharging resistance state of each of the MTJs; and assigning a logical state to each of said MTJs corresponding to said resistance state of said MTJ.

Abstract: A stochastic synapse for use in a neural network, comprising: first and second magnetic tunnel junction (MTJ) devices, each MTJ device having a fixed layer port and a free layer port; a first and second control circuit, each connected respectively to the free layer port of the first and second MTJ devices, wherein the fixed layer ports of the first and second MTJ devices are connected to each other; wherein the first and second control circuits are configured to perform a gated XNOR operation between synapse and activation values; and wherein an output of the gated XNOR is represented by the output current through both of the first and second MTJ devices.

Abstract: An indirectly heated phase change switch (IPCS) for radio frequency (RF) switching, comprises two RF ports and an RF path therebetween, a phase change material (PCM) located between the RF ports, an embedded heater separated from the PCM by a dielectric layer, the embedded heater configured to provide thermal actuation of the PCM to bring about phase changes between a crystalline state and an amorphous state to provide Ohmic switching, and a bias voltage on the IPCS across said RF path. The bias is provided during heating to improve the crystallization time.

Abstract: An analog to digital converter comprises an input for receiving an analog input signal; a plurality of outputs for outputting parallel bits of a digital signal that represents said analog input signal; and a neural network layer providing connections between each of said outputs respectively, each connection having an adjustable weighting. The synapses of the neural networks may be memristors and training may use online gradient descent.

Abstract: A method of using memristor aided logic (MAGIC), comprises connecting together two input and one output memristor between a bit line and a word line, each memristor having a high resistance state and a low resistance state, setting the output memristor to the low resistance state as an initiation state and then applying logic inputs to the input memristors. The output then depends on whether the logic inputs have set the output memristor to the high resistance state.

Abstract: An electronic memory block comprises phase change memory cells for memory storage and further phase change memory cells forming logic gates, to provide in-memory data processing.

Abstract: A digital to analog converter is constructed using a neural network layer. The converter has inputs for receiving parallel bits of a digital input signal and an output for outputting an analog signal which is based on the digital input. Connecting the input and the output is a neural network layer which is configured to convert the parallel bits into an output analog signal that is representative of the digital input signal. The neural network may be hardwired and the synapses may rely on memristors as programmable elements.

Abstract: A pipelined ADC system comprising: a first ADC stage comprising a trainable neural network layer and configured to receive an analog input signal, and convert it into a first n-bit digital output representing said analog input signal; a DAC circuit comprising a trainable neural network layer and configured to receive said first n-bit digital output, and convert it into an analog output signal representing said first n-bit digital output; and a second ADC stage comprising a trainable neural network layer and configured to receive a residue analog input signal of said analog input signal, and convert it into a second n-bit digital output representing said residue analog input signal; wherein said first and second n-bit digital outputs are combined to generate a combined digital output representing said analog input signal.

Abstract: A neural network comprising: a plurality of interconnected neural network elements, each comprising: a neuron circuit comprising a delta-sigma modulator, and at least one synapse device comprising a memristor connected to an output of said neuron circuit; wherein an adjustable synaptic weighting of said at least one synapse device is set based on said output of said neuron circuit

Abstract: A method, comprising: providing an electrical energy source having a specified amount of electrical energy; connecting an array comprising n magnetic tunnel junctions (MTJ) in parallel to said electrical energy source, wherein each of said MTJs is at a high resistance initial state; discharging said specified energy amount through said MTJs, thereby causing a random subset of said MTJs to switch to a lower resistance state; determining a post-discharging resistance state of each of the MTJs; and assigning a logical state to each of said MTJs corresponding to said resistance state of said MTJ.

Abstract: A stochastic synapse for use in a neural network, comprising: first and second magnetic tunnel junction (MTJ) devices, each MTJ device having a fixed layer port and a free layer port; a first and second control circuit, each connected respectively to the free layer port of the first and second MTJ devices, wherein the fixed layer ports of the first and second MTJ devices are connected to each other; wherein the first and second control circuits are configured to perform a gated XNOR operation between synapse and activation values; and wherein an output of the gated XNOR is represented by the output current through both of the first and second MTJ devices.

Abstract: A pipelined ADC system comprising: a first ADC stage comprising a trainable neural network layer and configured to receive an analog input signal, and convert it into a first n-bit digital output representing said analog input signal; a DAC circuit comprising a trainable neural network layer and configured to receive said first n-bit digital output, and convert it into an analog output signal representing said first n-bit digital output; and a second ADC stage comprising a trainable neural network layer and configured to receive a residue analog input signal of said analog input signal, and convert it into a second n-bit digital output representing said residue analog input signal; wherein said first and second n-bit digital outputs are combined to generate a combined digital output representing said analog input signal.

Abstract: A digital to analog converter is constructed using a neural network layer. The converter has inputs for receiving parallel bits of a digital input signal and an output for outputting an analog signal which is based on the digital input. Connecting the input and the output is a neural network layer which is configured to convert the parallel bits into an output analog signal that is representative of the digital input signal. The neural network may be hardwired and the synapses may rely on memristors as programmable elements.

Abstract: A hybrid computer that comprises a sequential processor, a single instruction massively parallel (SIMD) processor, and shared memory module that is shared between the sequential processor and the SIMD processor.

Abstract: Logic gates are made from first and second resistive elements connected together to form a voltage divider. One or both of the resistive elements is a unipolar memristor. OR and NOT gates may be constructed to make a digital logic system.

Abstract: An analog to digital converter comprises an input for receiving an analog input signal; a plurality of outputs for outputting parallel bits of a digital signal that represents said analog input signal; and a neural network layer providing connections between each of said outputs respectively, each connection having an adjustable weighting. The synapses of the neural networks may be memristors and training may use online gradient descent.

Abstract: Apparatus and method for hashing a message, comprises using an array of individually selectable memristor cells. The memristor cells are subject to write disturb that affects cells neighboring a selected cell so that a write operation into one cell has a knock-on effect on the neighbors. The array is initiated into a known stable state so that these changes to neighboring cells are predictable according to proximity to the currently selected cell. An inserter sequentially mixes bits with the hash so far to insert bits into successively selected cells of the memristor array and forms a succession of memristor array states including the knock on effects on the neighboring cells. A final resulting memristor array state following input of the bits forms the hash of the message.

Abstract: A method and a device that includes a set of multiple pipeline stages, wherein the set of multiple pipeline stages is arranged to execute a first thread of instructions; multiple memristor based registers that are arranged to store a state of another thread of instructions that differs from the first thread of instructions; and a control circuit that is arranged to control a thread switch between the first thread of instructions and the other thread of instructions by controlling a storage of a state of the first thread of instructions at the multiple memristor based registers and by controlling a provision of the state of the other thread of instructions by the set of multiple pipeline stages; wherein the set of multiple pipeline stages is arranged to execute the other thread of instructions upon a reception of the state of the other thread of instructions.

Abstract: A method and a device that includes a set of multiple pipeline stages, wherein the set of multiple pipeline stages is arranged to execute a first thread of instructions; multiple memristor based registers that are arranged to store a state of another thread of instructions that differs from the first thread of instructions; and a control circuit that is arranged to control a thread switch between the first thread of instructions and the other thread of instructions by controlling a storage of a state of the first thread of instructions at the multiple memristor based registers and by controlling a provision of the state of the other thread of instructions by the set of multiple pipeline stages; wherein the set of multiple pipeline stages is arranged to execute the other thread of instructions upon a reception of the state of the other thread of instructions.

Abstract: Logic gates are made from first and second resistive elements connected together to form a voltage divider. One or both of the resistive elements is a unipolar memristor. OR and NOT gates may be constructed to make a digital logic system.