Polyn Technology Limited

Vitro Flat Glass LLC

Boris Maslov

Aleksandrs Timofejevs

Dmitri Godovskiy

Nikolai Kovshov

Ivan Alexandrovich Sokol

Dmitrii Godovskii

Pavel Anatolyevich Zaikin

Dmitri Kossakovski

Systems, devices, integrated circuits, and methods are provided for analog hardware realization of neural networks. An electronic device includes a plurality of resistors corresponding to a plurality of weights of a neural network and one or more amplifiers coupled to the plurality of resistors. The plurality of resistors includes a first resistor corresponding to a first weight of the neural network. The one or more amplifiers and the plurality of resistors are configured to form a neural network circuit associated with the neural network. The first resistor has a variable resistance. In some embodiments, the first resistor is formed based on a crossbar array of resistive elements or a crossbar array of memory cells. Each resistive element or memory cell is located at a cross point of, and electrically coupled between, a respective word line and a respective bit line.

Analog hardware realization of neural networks having variable weights

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.

Systems and methods for optimizing energy efficiency of analog neuromorphic circuits

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.

Electrochromic devices, methods of manufacturing and operation thereof

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.

Sound signal processing using a neuromorphic analog signal processor

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.

Optimizations for analog hardware realization of trained neural networks

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.

Systems and methods for detonation control in spark ignition engines using analog neuromorphic computing hardware

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. The memory circuit is configured to (i) store weight values corresponding to connections of the analog neural network and (ii) supply different weight values to the control circuits for different time periods.

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Irvine, CA, United States of America

Boris Maslov