Abstract: Use of spoken input for user devices, e.g. smartphones, can be challenging due to presence of other sound sources. Blind source separation (BSS) techniques aim to separate a sound generated by a particular source of interest from a mixture of different sounds. Various BSS techniques disclosed herein are based on recognition that providing additional information that is considered within iterations of a nonnegative tensor factorization (NTF) model improves accuracy and efficiency of source separation. Examples of such information include direction estimates or neural network models trained to recognize a particular sound of interest. Furthermore, identifying and processing incremental changes to an NTF model, rather than re-processing the entire model each time data changes, provides an efficient and fast manner for performing source separation on large sets of quickly changing data. Carrying out at least parts of BSS techniques in a cloud allows flexible utilization of local and remote sources.

Abstract: An inference task is performed using a computation device having a plurality of processing elements operable in parallel and connected via a connectivity system. Performing the task includes accepting at the device a specification of at least part of the inference task. The specification characterizes a plurality of variables and a plurality of factors, each factor being associated with a subset of the variables. Each of the processing elements is configured with data defining one or more of the plurality of factors. At each of the processing elements, computation associated with one of the factors is performed concurrently with other of the processing elements performing computation associated with different ones of the factors. Messages are exchanged via a connectivity system. The messages provide inputs and/or outputs to the processing elements for the computations associated with the factors and provide a result of performing of the at least the part of the inference task.

Abstract: In an aspect, in general, a programmable computation device performs computations of an inference task specified by a plurality of variables and a plurality of factors, each factor being associated with a subset of the variables. The device includes one or more processing elements. Each processing element includes a first storage for a definition of a factor, a second storage for data associated with the inputs and/or outputs of at least some of the computations, and one or more computation units coupled to the first storage and the second storage for performing a succession of parts of the at least some of the computations that are associated with a factor, the succession of parts defined by data in the storage for the definition of the factor.

Abstract: A system is provided for updated processing of audio signals in a vehicle. The system includes a microphone, a transceiver and head unit. The microphone receives audio signals. The transceiver sends the received audio signals to a cloud computing system for processing, and receives the processed audio signals from the cloud computing system. The head unit receives the processed audio signals from the transceiver and plays the processed audio data through the vehicle's audio system.

Abstract: Microphone stages in a microphone array may be coupled together in a daisy chain. Each stage may include a microphone, an analog to digital converter, a decimation unit, a receiver, an adder, and a transmitter. The converter may convert analog audio microphone signals into digital codes that may be decimated. The adder may add decimated digital codes in each stage to a cumulative sum of decimated digital codes from prior stages. This new sum may be transmitted to the next microphone stage, where the adder may add the decimated digital codes from that stage to the cumulative sum. A serial interface may be used to connect the transmitters and receivers of each of the stages. The serial interface may be used to transmit the cumulative sum of decimated digital codes between the stages. The serial interface may also be used to transmit configuration data between the stages.

Abstract: In a data processing system, a method for implementing a factor graph having variable nodes and function nodes connected to each other by edges includes implementing a first function node and a on a first computer system, the first computer system being in network communication with a second computer system; establishing a network connection to each of a plurality of processing systems; receiving, at the first function node, soft data from a variable node implemented on one of the processing systems, the soft data including an estimate of a value and information representative of an extent to which the estimate is believed to correspond to a correct value; and transmitting, from the first function node to the one of the processing systems, soft data representing an updated estimate of the value.

Abstract: The present disclosure relates generally to improving acoustic source tracking and selection and, more particularly, to techniques for acoustic source tracking and selection using motion or position information. Embodiments of the present disclosure include systems designed to select and track acoustic sources. In one embodiment, the system may be realized as an integrated circuit including a microphone array, motion sensing circuitry, position sensing circuitry, analog-to-digital converter (ADC) circuitry configured to convert analog audio signals from the microphone array into digital audio signals for further processing, and a digital signal processor (DSP) or other circuitry for processing the digital audio signals based on motion data and other sensor data. Sensor data may be correlated to the analog or digital audio signals to improve source separation or other audio processing.

Abstract: A circuit implementing a soft logical processing network includes an interconnection of analog processing elements, which can include soft logic gates, for instance soft Equals gates and soft XOR gates. In some examples, each of the soft logic gates include multiple circuit parts, with each part including an input configured to accept a voltage signal representation of a soft logical quantity, and a conversion section configured to use the accepted voltage representation to form a corresponding current signal. The current signals are combined to form a signal representation of the output of the gate.

Abstract: A storage device includes a storage array having a group of storage elements. Each storage element can written to a discrete set of physical states. A read circuit selects one or more storage elements and generates, for each selected storage element, an analog signal representative of the physical state of the selected storage element. A signal processing circuit processes the analog signal to generate a plurality of outputs, with each output representing a degree of an association of the selected storage element with a different subset of one or more of the discrete set of physical states.

Abstract: A processor includes a first memory module for storing a first set of storage values each representing a respective input, and a second memory module for storing a second set of storage values in analog form. An analog module is coupled to the first and the second memory modules. The analog module is configured to, in each operation cycle of at least one iteration, update at least some of the second set of storage values based on the first and the second sets of storage values. An output module is for generating a set of outputs from at least some of the second set of storage values.

Abstract: A processor includes a first memory module for storing a first set of storage values each representing a respective input, and a second memory module for storing a second set of storage values in analog form. An analog module is coupled to the first and the second memory modules. The analog module is configured to, in each operation cycle of at least one iteration, update at least some of the second set of storage values based on the first and the second sets of storage values. An output module is for generating a set of outputs from at least some of the second set of storage values.

Abstract: In a data processing system, a method for implementing a factor graph having variable nodes and function nodes connected to each other by edges includes implementing a first function node and a on a first computer system, the first computer system being in network communication with a second computer system; establishing a network connection to each of a plurality of processing systems; receiving, at the first function node, soft data from a variable node implemented on one of the processing systems, the soft data including an estimate of a value and information representative of an extent to which the estimate is believed to correspond to a correct value; and transmitting, from the first function node to the one of the processing systems, soft data representing an updated estimate of the value.

Abstract: A method of executing operations in parallel in a probability processing system includes providing a probability processor for executing said operations; and providing a scheduler for identifying, from said operations, those operations that can be executed in parallel. Providing the scheduler includes compiling code written in a probability programming language, that includes both modeling instructions and instructions for scheduling.

Abstract: A processor includes a first memory module for storing a first set of storage values each representing a respective input, and a second memory module for storing a second set of storage values in analog form. An analog module is coupled to the first and the second memory modules. The analog module is configured to, in each operation cycle of at least one iteration, update at least some of the second set of storage values based on the first and the second sets of storage values. An output module is for generating a set of outputs from at least some of the second set of storage values.

Abstract: A processor implements a network of functional nodes and communication paths between the nodes. The processor includes a plurality of circuit implementations of the functional nodes of the processor; and a plurality of signal paths implementing the communication paths linking the circuit implementations of the nodes. At least some of the signal paths are configured to pass signal values represented according to temporal patterns of signal levels on the signal paths. The processor also includes a plurality of circuit components for conversion between a signal value represented as a signal level (e.g., voltage or current level) and a signal value represented as a temporal pattern.

Abstract: In one aspect, a storage device includes a plurality of storage strings, each comprising a serial interconnection of a plurality of active storage elements, each storage element having a part for maintaining a storage state and a part of modulating a current through the element according to the storage state. The device also includes mapping circuitry for selectively sensing a storage state of a storage element in a storage string by forming current though the storage element that is a non-linear function of the storage state. In some examples, the mapping circuitry comprises reference string of active elements, and the mapping circuitry selectively senses a storage state by forming a difference in currents in the sensed storage string and in the reference string that is a non-linear function of the storage state. In some examples, the active storage elements comprise floating gate transistors.

Abstract: An inference task is performed using a computation device having a plurality of processing elements operable in parallel and connected via a connectivity system. Performing the task includes accepting at the device a specification of at least part of the inference task. The specification characterizes a plurality of variables and a plurality of factors, each factor being associated with a subset of the variables. Each of the processing elements is configured with data defining one or more of the plurality of factors. At each of the processing elements, computation associated with one of the factors is performed concurrently with other of the processing elements performing computation associated with different ones of the factors. Messages are exchanged via a connectivity system. The messages provide inputs and/or outputs to the processing elements for the computations associated with the factors and provide a result of performing of the at least the part of the inference task.

Abstract: An approach to signal conversion adapts the signal conversion process, for example, by adapting or configuring signal conversion circuitry, according to inferred characteristics (e.g., probability distribution of value) of a signal being converted. As an example, an analog-to-digital converter (ADC) may be adapted so that its accuracy varies across the range of possible input signal values in such a way that on average the digital signal provides a higher accuracy than had the accuracy remained fixed. In another example, models (and corresponding inference circuitry) of both an input signal process and of a quantization process are used to improve signal conversion accuracy.

Abstract: A circuit implementing a soft logical processing network includes an interconnection of analog processing elements, which can include soft logic gates, for instance soft Equals gates and soft XOR gates. In some examples, each of the soft logic gates include multiple circuit parts, with each part including an input configured to accept a voltage signal representation of a soft logical quantity, and a conversion section configured to use the accepted voltage representation to form a corresponding current signal. The current signals are combined to form a signal representation of the output of the gate.

Abstract: The process of implementing a belief propagation network in software and/or hardware can begin with a factor-graph-designer who designs a factor graph that implements that network. A development system provides a user with a way to specify a factor graph at a high or abstract level, and then solve the factor graph, or make an instance of the factor graph in software and/or hardware based on the specification. Factor graphs enable designers to create a graphical model of complicated belief propagation networks such as Markov chains, hidden Markov models, and Bayesian networks.