Methods and systems for identifying quantisation parameters for a Deep Neural Network (DNN). The method includes determining an output of a model of the DNN in response to training data, the model of the DNN comprising one or more quantisation blocks configured to transform a set of values input to a layer of the DNN prior to processing the set of values in accordance with the layer, the transformation of the set of values simulating quantisation of the set of values to a fixed point number format defined by one or more quantisation parameters; determining a cost metric of the DNN based on the determined output and a size of the DNN based on the quantisation parameters; back-propagating a derivative of the cost metric to one or more of the quantisation parameters to generate a gradient of the cost metric for each of the one or more quantisation parameters; and adjusting one or more of the quantisation parameters based on the gradients.

The present disclosure includes apparatuses and methods related to an artificial intelligence accelerator in memory. An example apparatus can include a number of registers configured to enable the apparatus to operate in an artificial intelligence mode to perform artificial intelligence operations and an artificial intelligence (AI) accelerator configured to perform the artificial intelligence operations using the data stored in the number of memory arrays. The AI accelerator can include hardware, software, and or firmware that is configured to perform operations associated with AI operations. The hardware can include circuitry configured as an adder and/or multiplier to perform operations, such as logic operations, associated with AI operations.

The present disclosure relates to a method, a device and a computation apparatus for allocating a space address to data in a memory, where the computation apparatus is included in a combined processing apparatus, which includes a general interconnection interface and other processing apparatuses. The computation apparatus interacts with other processing apparatuses to jointly complete computations specified by the user. The combined processing apparatus also includes a storage apparatus. The storage apparatus is respectively connected to the computation apparatus and the other processing apparatuses, and is used for storing data of the computation apparatus and other processing apparatuses. The technical solutions of the present disclosure improve utilization of storage space of the memory.

The present invention relates to a neuromimetic network comprising a set of neurons and a set of synapses, at least one neuron comprising a first stack of superimposed layers, the first stack successively comprising: a first electrode, a first barrier layer made of an electrically insulating material, and a second electrode, the first electrode, the first barrier layer and the second electrode forming a first ferroelectric tunnel junction, at least one synapse comprising a second stack of superimposed layers, the second stack successively comprising: a third electrode, a second barrier layer made of an electrically insulating material, and a fourth electrode, the third electrode, the second barrier layer and the fourth electrode forming a second ferroelectric tunnel junction.

Dual-precision analog memory cells and arrays are provided. In some embodiments, a memory cell, comprises a non-volatile memory element having an input terminal and at least one output terminal; and a volatile memory element having a plurality of input terminals and an output terminal, wherein the output terminal of the volatile memory element is coupled to the input terminal of the non-volatile memory element, and wherein the volatile memory element comprises: a first transistor coupled between a first supply and a common node, and a second transistor coupled between a second supply and the common node; wherein the common node is coupled to the output terminal of the volatile memory element; and wherein gates of the first and second transistors are coupled to respective ones of the plurality of input terminals of the volatile memory element.

Dual-precision analog memory cells and arrays are provided. In some embodiments, a memory cell, comprises a non-volatile memory element having an input terminal and at least one output terminal; and a volatile memory element having a plurality of input terminals and an output terminal, wherein the output terminal of the volatile memory element is coupled to the input terminal of the non-volatile memory element, and wherein the volatile memory element comprises: a first transistor coupled between a first supply and a common node, and a second transistor coupled between a second supply and the common node; wherein the common node is coupled to the output terminal of the volatile memory element; and wherein gates of the first and second transistors are coupled to respective ones of the plurality of input terminals of the volatile memory element.

Artificial neuromorphic circuit includes synapse and post-neuron circuits. Synapse circuit includes phase change element, first switch having at least three terminals, and second switch. Phase change element includes first and second terminals. First switch includes first, second and control terminals. Second switch includes first, second and control terminals. First switch is configured to receive first pulse signal. Second switch is coupled to phase change element and first switch, and is configured to receive second pulse signal. Post-neuron circuit includes capacitor and input terminal. Input terminal of post-neuron circuit charges capacitor in response to first pulse signal. Post-neuron circuit generates firing signal based on voltage level of capacitor and threshold voltage. Post-neuron circuit generates control signal based on firing signal. Control signal controls turning on of second switch. Second pulse signal flows through second switch to control state of phase change element to determine weight of artificial neuromorphic circuit.

To compensate switching of a dielectric component of a non-linear polar material based capacitor, an explicit dielectric capacitor is added to a memory bit-cell and controlled by a signal opposite to the signal driven on a plate-line.

Some systems, devices and methods detailed herein provide a system for use in determining metrics of a subject. The system can provide, as an output, a function-metric value determined based on a defined relationship between physiological measures and a chronological age.

An operating method of a neuromorphic processor which processes data based on a neural network including a first layer including axons and a second layer including neurons includes receiving synaptic weights between the first layer and the second layer, decomposing the synaptic weights into presynaptic weights, a number of which is identical to a number of the axons, and postsynaptic weights, a number of which is identical to a number of the synaptic weights, and storing the presynaptic weights and the postsynaptic weights. A precision of each of the synaptic weights is a first number of bits, a precision of each of the presynaptic weights is a second number of bits, and a precision of each of the postsynaptic weights is a third number of bits. The third number of the bits is smaller than the first number of the bits.