Computing Equipment and Electronics

A technology of computing equipment and computing modules, which is applied in the directions of computing using non-number system representation, computing using random pulse sequence, biological neural network model, etc. , the effect of reducing delay and avoiding resource consumption

Active Publication Date: 2021-10-15
HENGDU SYNSENSE TECH CO LTD
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Problems solved by technology

[0015] Temporal convolution is a common basic information processing method in neural networks, such as the aforementioned WaveNet, whi

Method used

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Embodiment Construction

[0086] The "pulse" mentioned anywhere in the present invention refers to the spike in the field of imitation, which is also called "peak". The training algorithm can be written as a computer program in the form of computer code, stored in a storage medium, and read by a computer (such as a high-performance graphics processor GPU device, a field programmable gate array FPGA, an application-specific integrated circuit ASIC, etc.) Take, under the training of training data (various data sets) and training algorithms, obtain neural network configuration parameters that can be deployed to simulated neuromorphic devices (such as brain-like chips). The simulating device configured with this parameter will gain reasoning ability. According to the input signal of the sensor (such as the dynamic visual camera DVS that perceives the change of light and dark, the special sound signal acquisition equipment, etc.), the simulating device will reason it and output ( Such as wires, wireless com...

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Abstract

The invention discloses a computing device and an electronic device. The computing device includes several computing modules, each of which includes several neuron clusters, and the computing modules are configured as follows: weighted by a first weight matrix, the the input pulse train of the block is projected to the first neuron population through polysynaptic projections, wherein the polysynaptic projections have at least two different synaptic time constants and the two different synaptic time constants are both positive, Or at least two different synaptic transmission delays. In order to realize time-domain convolution in the spiking neural network with low hardware resource consumption, the present invention discloses the multi-synaptic projection technology with different synaptic time constants, and further proposes a method based on residual connection, jumping Waveform-aware spiking neural networks for time-domain signal processing characterized by connections etc. Through these technical means, the performance gap between the SNN and the ANN is bridged, and the SNN whose performance reaches or approaches that of the ANN is obtained.

Description

technical field [0001] The invention relates to a computing device and an electronic device, in particular to a training device, a chip and an electronic device configured with a pulse neural network SNN or an artificial neural network ANN. Background technique [0002] The traditional artificial neural network (Artificial Neural Network, ANN) belongs to the second-generation neural network, and the deep convolutional neural network (existing technology 1) as the representative has led the development of artificial intelligence in the past ten years. [0003] Prior art 1: Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Advances in neuralinformation processing systems, 2012, 25: 1097-1105. [0004] The second-generation neural network generally takes the pursuit of accuracy as the primary goal (high performance model), so it usually has the characteristics of high energy consumption and high storage space consumption,...

Claims

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Application Information

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IPC IPC(8): G06F7/70
CPCG06N3/049G06N3/045
Inventor 乔宁西克·萨迪克·尤艾尔阿明白鑫魏德尔·菲利普
Owner HENGDU SYNSENSE TECH CO LTD
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