Unlock instant, AI-driven research and patent intelligence for your innovation.

Implementation of State Quantized Networks in Cross-Array Neuromorphic Hardware

A cross-array, neuromorphic technology, applied in the field of neural networks, to achieve the effect of reducing scale

Active Publication Date: 2022-04-26
UNIV OF ELECTRONICS SCI & TECH OF CHINA
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In the currently implemented cross-array neuromorphic hardware, parameters such as synaptic weights and neuron parameters such as thresholds, leakage constants, set voltages, refractory periods, and synaptic delays require a lot of system storage resources. With the rapid expansion of circuit scale, this will inevitably become a major bottleneck of neuromorphic hardware under the condition that storage resources are relatively scarce today.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Implementation of State Quantized Networks in Cross-Array Neuromorphic Hardware
  • Implementation of State Quantized Networks in Cross-Array Neuromorphic Hardware
  • Implementation of State Quantized Networks in Cross-Array Neuromorphic Hardware

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0035] refer to figure 1 , the embodiment of the present invention provides a method for implementing a state quantization network in a cross-array neuromorphic hardware, comprising the following steps:

[0036] S1: Select parameters and quantize them. Parameter quantization can be performed after the neural network training is completed, or during neural network training.

[0037] A: Quantize after the neural network training is complete

[0038] The artificial neural network (including MLP, CNN, RNN, LSTM, etc.) is trained to obtain parameters under specific tasks and conditions, (including weights, thresholds, leakage constants, set voltage values, refractory period duration, synaptic delay duration, etc.);

[0039] The artificia...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention belongs to the technical field of neural networks, and relates to a method for realizing a state quantization network in cross-array neuromorphic hardware. The method of the present invention is, after quantifying the parameters of the artificial neural network (weights, thresholds, leakage constants, set voltage values, refractory period duration, synaptic delay duration and other parameters), the quantified parameters Mapping to the cross-array neuromorphic hardware, and then sending the preprocessed input data to the cross-array neuromorphic hardware can realize the state quantization network. Through state quantization, the requirements of the cross-array neuromorphic hardware on the size of the storage unit, the number of storage stages, and reliability are effectively reduced.

Description

technical field [0001] The invention belongs to the technical field of neural networks, and relates to a method for realizing a state quantization network in cross-array neuromorphic hardware. Background technique [0002] Neuromorphic computing is used to refer to brain-derived computers, devices, and models in contrast to the prevalent von Neumann computer architecture. This biomimetic approach creates highly connected synthetic neurons and synapses that can be used for theoretical modeling in neuroscience to solve machine learning problems. [0003] Neuromorphic circuit is one of the physical realizations of the neural network model. It abstracts and simulates the biological nervous system at a high level by means of hardware, in order to achieve low power consumption, High adaptability and other characteristics. [0004] Crossbars use memristors for data storage and parallel computing and as an important component architecture of neural network nodes is to use crossbar...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/06G06N3/063
CPCG06N3/061G06N3/063
Inventor 胡绍刚罗鑫乔冠超刘益安张成明刘洋于奇
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA