rus
Issue #1/2021
S.Y.Udovichenko, A.D.Pisarev, A.N.Busygin, A.N.Bobylev
Biomorphous neuroprocessor – prototype of a new generation computer being a carrier of artificial intelligence. Part 2
Biomorphous neuroprocessor – prototype of a new generation computer being a carrier of artificial intelligence. Part 2
DOI: 10.22184/1993-8578.2021.14.1.68.79
Primary and ultimate information processing takes place in the input and output devices of the biomorphic neuroprocessor. The results are presented on the compression of digital information at the input and its coding into pulses, as well as on the decoding of information about the activation of neurons at the output into a digital binary code. An implementation of a hardware neural network of a processor based on an original biomorphic electrical model of a neuron is presented. The results of SPICE modeling and experimental research of signal processing processes in the modes of routing neuron output pulses to synapses of other neurons in a logical matrix, scalar multiplication of a matrix of numbers by a vector, and associative self-learning in a memory matrix are presented. For the first time, the generation of a new association (new knowledge) was demonstrated both in computer simulation and in a fabricated memristor-diode crossbar, in contrast to self-learning in existing hardware neural networks with synapses based on discrete memristors.
Primary and ultimate information processing takes place in the input and output devices of the biomorphic neuroprocessor. The results are presented on the compression of digital information at the input and its coding into pulses, as well as on the decoding of information about the activation of neurons at the output into a digital binary code. An implementation of a hardware neural network of a processor based on an original biomorphic electrical model of a neuron is presented. The results of SPICE modeling and experimental research of signal processing processes in the modes of routing neuron output pulses to synapses of other neurons in a logical matrix, scalar multiplication of a matrix of numbers by a vector, and associative self-learning in a memory matrix are presented. For the first time, the generation of a new association (new knowledge) was demonstrated both in computer simulation and in a fabricated memristor-diode crossbar, in contrast to self-learning in existing hardware neural networks with synapses based on discrete memristors.
Теги: biomorphic neuroprocessor compression encoding and decoding information hardware testing input and output devices membrane-diode crossbar modeling signal processing аппаратное тестирование биоморфный нейпроцессор входное и выходное устройства кодирование и декодирование информации мемристорно-диодный кроссбар моделирование обработка сигналов сжатие
BIOMORPHOUS NEUROPROCESSOR – PROTOTYPE OF A NEW GENERATION COMPUTER BEING A CARRIER OF ARTIFICIAL INTELLIGENCE. PART 2
Primary and ultimate information processing takes place in the input and output devices of the biomorphic neuroprocessor. The results are presented on the compression of digital information at the input and its coding into pulses, as well as on the decoding of information about the activation of neurons at the output into a digital binary code. An implementation of a hardware neural network of a processor based on an original biomorphic electrical model of a neuron is presented. The results of SPICE modeling and experimental research of signal processing processes in the modes of routing neuron output pulses to synapses of other neurons in a logical matrix, scalar multiplication of a matrix of numbers by a vector, and associative self-learning in a memory matrix are presented. For the first time, the generation of a new association (new knowledge) was demonstrated both in computer simulation and in a fabricated memristor-diode crossbar, in contrast to self-learning in existing hardware neural networks with synapses based on discrete memristors.
Primary and ultimate information processing takes place in the input and output devices of the biomorphic neuroprocessor. The results are presented on the compression of digital information at the input and its coding into pulses, as well as on the decoding of information about the activation of neurons at the output into a digital binary code. An implementation of a hardware neural network of a processor based on an original biomorphic electrical model of a neuron is presented. The results of SPICE modeling and experimental research of signal processing processes in the modes of routing neuron output pulses to synapses of other neurons in a logical matrix, scalar multiplication of a matrix of numbers by a vector, and associative self-learning in a memory matrix are presented. For the first time, the generation of a new association (new knowledge) was demonstrated both in computer simulation and in a fabricated memristor-diode crossbar, in contrast to self-learning in existing hardware neural networks with synapses based on discrete memristors.
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