Abstract: A neural network has a plurality of layers. Each layer includes a kernel module configured to store and to process in an event-driven fashion kernel values of at least one convolution kernel; a neuron module configured to store and to process in an event-driven fashion neuron states of neurons of the network; and a memory mapper configured to determine neurons to which an incoming a spike event from a source layer projects to a convolution, with the at least one convolution kernel. Neuron states of said determined neurons are updated with applicable kernel values of the at least one convolution kernel. The memory mapper is configured to process incoming spike events in an event-driven fashion.

Abstract: A quantum computing system is set forth, comprising a photon source for generating short duration single photon pulses, at least one temporal interferometric network for time-bin encoding each short duration single photon pulse in a single spatial mode, wherein the temporal interferometric network includes at least one optical switch, at least one birefringent material and at least one polarization element, and a photon detector for detecting time-of-arrival of photons output from the temporal interferometric network to measure the state of the photons.

Abstract: A method by a router component in a multi-tenant on-demand serving infrastructure to route scoring requests to scoring containers. The method includes receiving a scoring request, determining a machine learning application associated with the scoring request, determining whether a router instance for the machine learning application exists, and responsive to a determination that a router instance for the machine learning application does not exist, obtaining a configuration object for the machine learning application and instantiating the router instance for the machine learning application based on the configuration object for the machine learning application. The method further includes invoking the router instance for the machine learning application to route the scoring request associated with the machine learning application to a scoring container that provides scoring functionality for the machine learning application.

Abstract: Devices and techniques are generally described for compression of machine learning models. In some examples, a first weight value wi of a machine learning model is determined. The first weight value wi may be associated with a first region comprising an interval of numbers. A first loss for the first weight value wi may be determined using a first loss function comprising a weighting term comprising a sinusoid function with an argument of ??rwi. ? may be selected such that a maximum value of the weighting term for the first region is of a format that is compatible with first machine learning accelerator. A gradient of the first loss function may be determined. A second weight value wi may be determined using the gradient and the first loss. The second weight value wi may be stored in a first memory.

Abstract: Methods, systems and apparatus for the analysis of biological samples. Biological sample held in sample plates may be processed by a PCR machine. PCR curves are generated for each biological sample and analyzed by one or more machine learning models. The PCR curves are assigned a confidence level and classified based on the analysis and the confidence level. PCR curve with a confidence level below a predetermined threshold may be flagged for analysis by a lab director. PCR curves flagged for manual analysis may be displayed on an analysis interface. The lab director may view, analyze and classify the flagged PCR curves through interaction with the analysis interface.

Abstract: A neuromorphic devices may be formed having a three-dimensional stacked structure. The neuromorphic device may include a lower device formed on a substrate, an interlayer insulating layer formed on the substrate to cover the lower device, a synapse device having a Schottky barrier transistor structure formed on the interlayer insulating layer, and a vertical connection wiring formed in the interlayer insulating layer to electrically connect the lower device and the synapse device. The synapse device may include a channel, a source having a metal silicide forming a first Schottky junction with the channel, a drain having a metal silicide forming a second Schottky junction with the channel, a floating gate for a synaptic operation, and a control gate. The synapse device may be formed using only low-temperature processes performed at less than about 500 degrees Celsius.

Abstract: A computing system operates a machine learning model comprising base parameters and adapter parameters applied as additive low-rank deltas to one or more transformations. The system determines a routing certificate for each request using a scope function and applies only an adapter state keyed to the determined routing certificate during inference. When a supervision signal is received, the system computes a candidate update and evaluates a gating policy that authorizes the commit only when confidence, risk, scope eligibility, and budget conditions are satisfied. Authorized updates are constrained by a projection operator and committed as delta transactions to a versioned transaction log with provenance metadata. The system supports deterministic rollback to any prior adapter version by computing a prefix sum of logged deltas from a baseline adapter state, without modifying the base parameters.

Abstract: A system for rendering an explanation output for users regarding an anomaly predicted by an anomaly detection module on the basis of high frequency sensor data or values derived therefrom in an industrial production process, wherein the anomaly detection module predicts the anomalies when the anomaly detection module classifies sensor data or ranges of sensor data that describe a state of a machine, a component, and/or a product of the production process as different from the data that are normally expected, wherein the system is configured to send an optimized explanation mask as a rendering of the explanation output for a user, wherein the user can identify which sensor data, ranges of sensor data, or values derived therefrom are responsible for the anomaly predicted by the anomaly detection module based on the optimized explanation mask.

Abstract: Systems and methods are disclosed for performing fault detection and prediction for power electronics and switching devices for power electronics, such as power inverters. Systems and methods disclosed herein can include determining, by a partial least squares model that evaluates values for one or more switching parameters for a switching device, the one or more switching parameters selected from a first set of switching parameters, a predicted value for the on-state current Ids of the switching device. The predicted value for the on-state current Ids can be based on the values of the one or more switching parameters for the switching device. Systems and methods disclosed herein can determine a residual comprising the difference between the predicted value for the another switching parameter of the switching device and an actual value of the predicted value for the another switching parameter, and generate a test statistic based on the residual.

Abstract: Computer systems and computer-implemented methods modify a machine learning network, such as a deep neural network, to introduce judgment to the network. A “combining” node is added to the network, to thereby generate a modified network, where activation of the combining node is based, at least in part, on output from a subject node of the network. The computer system then trains the modified network by, for each training data item in a set of training data, performing forward and back propagation computations through the modified network, where the backward propagation computation through the modified network comprises computing estimated partial derivatives of an error function of an objective for the network, except that the combining node selectively blocks back-propagation of estimated partial derivatives to the subject node, even though activation of the combining node is based on the activation of the subject node.

Abstract: Systems, apparatuses, and methods for implementing memory bandwidth reduction techniques for low power convolutional neural network inference applications are disclosed. A system includes at least a processing unit and an external memory coupled to the processing unit. The system detects a request to perform a convolution operation on input data from a plurality of channels. Responsive to detecting the request, the system partitions the input data from the plurality of channels into 3D blocks so as to minimize the external memory bandwidth utilization for the convolution operation being performed. Next, the system loads a selected 3D block from external memory into internal memory and then generates convolution output data for the selected 3D block for one or more features. Then, for each feature, the system adds convolution output data together across channels prior to writing the convolution output data to the external memory.

Abstract: Techniques are described herein for provided for augmenting graph networks. Upon receiving a request from an edge device, a graph system prompts a generative model and compares its output to a first graph network of nodes and edges based on similarity scores and degrees of separation. If an adequate response is not found, the graph system identifies and evaluates a second set of nodes outside the first graph network, extracts relevant metadata, and creates a second graph network with new relationships. Contextual natural language is generated from the metadata to form a response, which is returned to the edge device, and may involve controlling one or more assets or devices in response to the context of the request and/or the response.

Abstract: Neuron circuits are provided for spiking neural network apparatus having multiple such neuron circuits interconnected by links, each associated with a respective weight, for transmission of signals between neuron circuits. A neuron circuit includes a digital transmitter for generating trigger signals, indicating a state of the neuron circuit, on outgoing links of the circuit. The state is encoded in a time interval defined by these trigger signals. The neuron circuit includes a digital receiver for detecting such trigger signals on incoming links of the circuit, and digital accumulator logic. In response to detecting a trigger signal on an incoming link, the digital accumulator logic is adapted to generate a weighted signal dependent on the time interval and to accumulate the weighted signals generated from trigger signals on the incoming links to determine the state of the neuron circuit.

Abstract: Systems, methods, and computer program products train a residual neural network including a first fully connected layer, a first recurrent neural network layer, and at least one skip connection for anomaly detection. The at least one skip connection directly connects at least one of (i) an output of the first fully connected layer to a first other layer downstream of the first recurrent neural network layer in the residual neural network and (ii) an output of the first recurrent neural network layer to a second other layer downstream of a second recurrent neural network layer in the residual neural network.

Abstract: Communication-capable devices such as commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) systems to jointly exchange data and monitor environment. Such devices typically require diverse signal processing such as machine learning inference that demands high-power operations for real-time sensing and computing. The present invention provides a way to realize energy-efficient computing by exploiting the capability of data communications to access distributed computing resources including classical computers and quantum computers over networks. The system and method are based on the realization that computationally intensive processing is offloaded to networked hybrid classical-quantum computing to build dynamic computing graphs. Some embodiments use automated classical-quantum machine learning whose circuits and hyperparameters are automatically adjusted via gradient or heuristic optimization for Wi-Fi indoor monitoring and human tracking.

Abstract: A computer-implemented approach for integrating quantum computing elements into a neural network architecture, the neural network including an encoder and a decoder, the encoder being used to encode data input into the neural network and the decoder being used to at least partially reconstruct the encoded data. The encoder features at least one layer made up of quantum-based processors and at least one layer made up of non-quantum-based processors. This approach allows for extremely secure data transfer with high data compression.

Abstract: Spiking events in a spiking neural network may be processed via a memory system. A memory system may store data corresponding to a group of destination neurons. The memory system may, at each time interval of a SNN, pass through data corresponding to a group of pre-synaptic spike events from respective source neurons. The data corresponding to the group of pre-synaptic spike events may be subsequently stored in the memory system.

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for accurately and efficiently generating groups of images portraying semantically similar objects for utilization in building machine learning models. In particular, the disclosed system utilizes metadata and spatial statistics to extract semantically similar objects from a repository of digital images. In some embodiments, the disclosed system generates color embeddings and content embeddings for the identified objects. The disclosed system can further group similar objects together within a query space by utilizing a clustering algorithm to create object clusters and then refining and combining the object clusters within the query space. In some embodiments, the disclosed system utilizes one or more of the object clusters to build a machine learning model.

Abstract: A neuron circuit, which electronically applies the working principle of the neurons in human brain, controls an input signal according to a set threshold value, and allows to provide an output signal above the threshold value. The neuron circuit controls an input signal according to a set threshold value and allows for an output signal above the threshold value, for determining the size of the threshold value of the circuit, and has at least one threshold resistor, at least one bias resistor, at least one decaying resistor, and at least one switching unit connected to at least one of these resistors.

Abstract: Systems and methods for extracting rule lists from tree ensembles are provided. A system extracts first stage candidate rules from individual trees. The system identifies the first stage candidate rules that satisfy a precision threshold and places those rules in a solution set. Subsequently, a determination is made whether a further stage is needed based on whether a predetermined number of positive data samples of the data set are covered by the solution set. In the further stage, the system generates next stage candidate rules from previous stage candidate rules that have not been pruned and identifies the next stage candidate rules that satisfy the precision threshold, placing those rules in the solution set. A simplified rule list is generated by identifying a minimum subset of rules in the solution set that covers the positive data samples within the precision threshold.