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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Decoupled neural network training with re-computation and weight prediction.

Jiawei Peng¹, Yicheng Xu¹, Zhiping Lin¹

¹School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.

|February 23, 2023

Summary

This study introduces a decoupled neural network training scheme with re-computation and weight prediction (DTRP) to overcome backpropagation limitations. DTRP effectively reduces memory usage and accelerates training without sacrificing accuracy.

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Area of Science:

Artificial Intelligence
Machine Learning
Deep Learning

Background:

Backpropagation (BP) in neural network training faces limitations like accuracy drops or increased memory usage with existing decoupled learning methods.
Addressing these challenges is crucial for efficient and effective neural network model development.

Purpose of the Study:

To propose a novel decoupled learning method, the decoupled neural network training scheme with re-computation and weight prediction (DTRP).
To solve the memory explosion problem and weight delay issues inherent in decoupled training.

Main Methods:

The proposed DTRP utilizes a re-computation scheme to manage memory usage.
A weight prediction scheme addresses potential delays caused by re-computation.
A batch compensation scheme is implemented to enhance training speed.

Main Results:

Theoretical analysis indicates DTRP converges to critical points under specific conditions.
Experiments on convolutional neural networks demonstrate comparable or superior performance to state-of-the-art methods and standard BP.
The method effectively resolves memory explosion and achieves significant training acceleration.

Conclusions:

DTRP offers an effective solution for overcoming backpropagation limitations in neural network training.
The proposed scheme balances memory efficiency, accuracy, and computational speed.
DTRP presents a promising alternative for training deep learning models.