Polyn Technology Limited

Nikolai Kovshov

Aleksandrs Timofejevs

Dmitri Godovskiy

Boris Maslov

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

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.

Integrated circuits for neural networks

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.

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Moscow, Russia

Nikolai Kovshov