US 12,762,383 B2Granted

Computing device and electronic device

OwnerNing Qiao, Sadique UlAmeen Sheik +2inventors

InventorsNing Qiao, Sadique UlAmeen Sheik, Xin Bai, Philipp Weidel

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Abstract

A computing device includes computing modules, each of which comprises a plurality of neuron populations. The computing module is configured to project the input spike train which is weighted by a first weight matrix to a first neuron population through a multi-synapse projection. The multi-synapse projection has at least two different and positive synaptic time constants, or at least two different synaptic transmission delays. In order to realize time-domain convolution in a spiking neural network with low hardware resource consumption, a multi-synaptic projection technology means with different synaptic time constants is proposed. On this basis, a waveform-aware spike neural network for time-domain signal processing characterized by residual connections and skip connections is further proposed. Through these technical means, the performance gap between SNN and ANN is bridged, and SNN with performance reaching or close to ANN is obtained.

Claims20

Dependent claims

A computer neuromorphic chip, configured to process ambient signals, the neuromorphic chip comprising a plurality of hardware-implemented computing blocks fabricated on the chip, wherein each of the computing blocks comprises a plurality of hardware spiking neuron circuit populations formed on the chip, and each of the computing blocks receives a corresponding input spikes train;

wherein at least one of the computing blocks is configured to:
project the input spike train which is weighted by a first weight matrix to a first neuron population through a multi-synapse projection, wherein the multi-synapse projection comprises:
(i) at least two different synaptic time constants, and the two different synaptic time constants are both positive values;
or
(ii) at least two different synaptic transmission delays;
or
(iii) at least one positive synaptic time constant and at least one synaptic transmission delay with unequal delay durations;
project a spike train outputted by the first neuron population, which is weighted by a second weight matrix, to a second neuron population;
project the spike train outputted by the first neuron population, which is weighted by a third weight matrix, to a third neuron population;
add the input spike train of the computing block and an output spike train of the second neuron population to obtain an output spike train of the computing block, and using the output spike train of the computing block as a corresponding input spike train of a next computing block;
and
sum spike trains outputted by peer neurons in the third neuron population of the plurality of computing blocks to obtain a first spike train.

9 more elements

Dependencies are inferred from the claim text.