Abstract: An apparatus is provided for detonation control in spark ignition engines. The apparatus includes an analog neurocomputing hardware device, a knock sensor coupled to a spark ignition engine, an ignition coil for the spark ignition engine, and an Electronic Control Unit (ECU) for the spark ignition engine. The analog neuromorphic hardware device is configured to receive knock signals from the knock sensor, receive ignition coil data from the ignition coil, determine a knock level and ignition quality measure based on the received knock sensor signals and the received ignition coil data, and transmit the knock level and ignition quality measure to the ECU.

Abstract: Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology into an equivalent analog network of analog components. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection between analog components of the equivalent analog network. The method also includes generating a schematic model for implementing the equivalent analog network based on the weight matrix, including selecting component parameter values for the analog components.

Abstract: Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology into an equivalent analog network of analog components. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection between analog components of the equivalent analog network. The method also includes generating a schematic model for implementing the equivalent analog network based on the weight matrix, including selecting component parameter values for the analog components.

Abstract: Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology into an equivalent analog network of analog components. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection between analog components of the equivalent analog network. The method also includes generating a schematic model for implementing the equivalent analog network based on the weight matrix, including selecting component parameter values for the analog components.

Abstract: A neuromorphic analog signal processor includes a flexible circuit corresponding to an analog neural network. The flexible circuit includes operational amplifiers, each operational amplifier corresponding to an analog neuron. The flexible circuit also includes photoresistors or photodiodes interconnecting the operational amplifiers, and illumination sources. Each illumination source transmits light to a corresponding photoresistor or photodiode, thereby changing the resistance as a function of brightness of applied light. The flexible circuit also includes control circuits, each control circuit configured to apply a pulse-width modulation corresponding to a weight value, thereby causing pulsed signals at the illumination sources. The flexible circuit also includes a memory circuit coupled to the circuits.

Abstract: A hybrid analog-digital hardware apparatus and a method for realizing the hardware apparatus are provided. The hardware apparatus includes an analog circuit that includes a plurality of operational amplifiers and a plurality of resistors. The analog circuit is configured to receive an analog signal from one or more sensors, and compute an analog output based on the analog signal, by performing a portion of a trained neural network. In some implementations, the hardware apparatus includes an analog-to-digital converter coupled to the analog circuit and configured to receive and convert the analog output to a digital input. The hardware apparatus also includes a classifier or regression circuit coupled to the analog circuit. The classifier or regression circuit is configured to receive output (e.g., a set of embeddings) from the analog circuit, and classify the output to obtain a result according to a machine learning model.

Abstract: Systems and methods are provided for sound signal processing using neuromorphic analog signal processors. A hardware apparatus includes a digital switch coupled to a plurality of analog neuromorphic cores. The digital switch is configured to obtain one or more sound streams from one or more sound sources, transmit data based on the one or more sound streams to the plurality of analog neuromorphic cores, receive output from the plurality of analog neuromorphic cores, and output one or more modified sound streams based on the output received from the plurality of analog neuromorphic cores. Each analog neuromorphic core includes a respective analog network of analog components and is configured to (i) receive respective input data from the digital switch, (ii) perform a respective voice-related function on the respective input data, and (iii) transmit respective output to the digital switch.

Abstract: Systems, methods, and devices are provided for predictive maintenance of machines. An example apparatus includes a vibration sensor configured to sense vibrations of a vibration source and an analog circuit. The analog circuit comprises a plurality of operational amplifiers and a plurality of resistors. The analog circuit is coupled to the vibration sensor and configured to: receive an analog signal from the vibration sensor; and compute an output based on the analog signal, by performing a portion of a trained neural network.

Abstract: Electrochromic devices and components thereof and systems and methods for controlling electrochromic devices are disclosed. Further, electrochromic materials, electrochromic compositions and electrochromic layers useful for the devices and systems can be in the form of a gel. The present disclosure also provide methods to fabricate electrochromic devices and components thereof, electrochromic compositions, layers and gels.

Abstract: Systems, methods, and devices are provided for human activity recognition. An example device includes an integrated circuit for human activity recognition. The integrated circuit includes an analog network of analog components configured to implement a trained neural network model (e.g., an autoencoder) that is trained to generate a plurality of descriptors for a plurality of predefined human activities based on a plurality of features extracted from a plurality of electrical signals from one or more sensors. The device also includes one or more digital components configured to classify human activity (e.g., using a classifier, such as K-Nearest Neighbor) as one of the plurality of predefined human activities according to the plurality of descriptors generated by the integrated circuit. In some implementations, the device further includes the one or more sensors configured to collect the plurality of electrical signals during the human activity.

Abstract: An apparatus is provided for detonation control in spark ignition engines. The apparatus includes an analog neurocomputing hardware device, a knock sensor coupled to a spark ignition engine, an ignition coil for the spark ignition engine, and an Electronic Control Unit (ECU) for the spark ignition engine. The analog neuromorphic hardware device is configured to receive knock signals from the knock sensor, receive ignition coil data from the ignition coil, determine a knock level and ignition quality measure based on the received knock sensor signals and the received ignition coil data, and transmit the knock level and ignition quality measure to the ECU.

Abstract: Systems and methods are provided for optimizing energy efficiency of analog neuromorphic circuits. The method includes obtaining an integrated circuit implementing an analog network of analog components including operational amplifiers and resistors. The analog network represents a trained neural network, each operational amplifier represents an analog neuron, and each resistor represents a connection between two analog neurons. The method also includes generating inferences using the integrated circuit for test inputs, including simultaneously transferring signals from one layer to a subsequent layer. The method also includes, while generating inferences: in accordance with a determination that a level of signal output of the operational amplifiers is equilibrated: determining an active set of analog neurons of the analog network influencing signal formation for propagation of signals; and turning off power for other analog neurons of the analog network, for a predetermined period of time.

Abstract: Systems and methods are provided for analog hardware realization of convolutional neural networks for voice clarity. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology to an equivalent analog network of analog components. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. Each element of the weight matrix represents one or more connections between analog components of the equivalent analog network. The method also includes generating a schematic model for implementing the equivalent analog network based on the weight matrix, including selecting component values for the analog components.

Abstract: Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology to an equivalent analog network of analog components including a plurality of operational amplifiers and a plurality of resistors. Each operational amplifier represents an analog neuron of the equivalent analog network, and each resistor represents a connection between two analog neurons. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection. The method also includes generating a resistance matrix for the weight matrix. Each element of the resistance matrix corresponds to a respective weight of the weight matrix and represents a resistance value.

Abstract: Systems and methods are provided for generating libraries for hardware realization of neural networks. The method includes obtaining a plurality of neural network topologies. Each neural network topology corresponds to a respective neural network. The method also includes transforming each neural network topology to a respective equivalent analog network of analog components. The method also includes generating a plurality of lithographic masks for fabricating a plurality of circuits.

Abstract: Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes calculating one or more connection constraints based on analog integrated circuit (IC) design constraints. The method also includes transforming the neural network topology to an equivalent sparsely connected network of analog components satisfying the one or more connection constraints. The method also includes computing a weight matrix for the equivalent sparsely connected network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection between analog components of the equivalent sparsely connected network.

Abstract: Systems and methods are provided for analog hardware realization of neural networks. The method includes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology to an equivalent analog network of analog components including operational amplifiers and resistors. Each operational amplifier represents an analog neuron of the equivalent analog network, and each resistor represents a connection between two analog neurons. The method also includes computing a weight matrix based on the weights of the trained neural network. The method also includes generating a resistance matrix for the weight matrix. The method also includes pruning the equivalent analog network to reduce the number of operational amplifiers or the resistors, based on the resistance matrix, to obtain an optimized analog network of analog components.

Abstract: An integrated circuit includes an analog network of analog components fabricated by a method. The method includes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology to an equivalent analog network of analog components including operational amplifiers and resistors. Each operational amplifier represents an analog neuron, and each resistor represents a connection between analog neurons. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. The method also includes generating a resistance matrix for the weight matrix. The method also includes generating lithographic masks for fabricating a circuit implementing the equivalent analog network based on the resistance matrix. The method also includes fabricating the circuit based on the one or more lithographic masks using a lithographic process.

Abstract: Systems and methods are provided for analog hardware realization of neural networks. The method incudes obtaining a neural network topology and weights of a trained neural network. The method also includes transforming the neural network topology to an equivalent analog network of analog components. The method also includes computing a weight matrix for the equivalent analog network based on the weights of the trained neural network. Each element of the weight matrix represents a respective connection between analog components of the equivalent analog network. The method also includes generating a schematic model for implementing the equivalent analog network based on the weight matrix, including selecting component values for the analog components.

Abstract: Described herein are embodiments of systems and methods for oxidizing gases. In some embodiments, a reaction chamber is configured to receive a fuel gas and maintain the gas at a temperature within the reaction chamber that is above an autoignition temperature of the gas. The reaction chamber may also be configured to maintain a reaction temperature within the reaction chamber below a flameout temperature. In some embodiments, heat and product gases from the oxidation process can be used, for example, to drive a turbine, reciprocating engine, and injected back into the reaction chamber.