Summary of Neuroflux: Memory-efficient Cnn Training Using Adaptive Local Learning, by Dhananjay Saikumar and Blesson Varghese
NeuroFlux: Memory-Efficient CNN Training Using Adaptive Local Learning
by Dhananjay Saikumar, Blesson Varghese
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary NeuroFlux is a novel Convolutional Neural Network (CNN) training system designed for resource-constrained mobile and edge environments. The traditional backpropagation approach is memory-intensive, requiring smaller batch sizes to accommodate GPU memory limitations, resulting in prolonged training times. NeuroFlux addresses this challenge by introducing two innovative techniques: adaptive auxiliary networks that reduce GPU memory usage and block-specific adaptive batch sizes that accelerate the training process. By segmenting a CNN into blocks based on GPU memory usage and attaching an auxiliary network to each layer, NeuroFlux enables “adaptive local learning.” Additionally, NeuroFlux caches intermediate activations, eliminating redundant forward passes and further speeding up training. Compared to backpropagation, NeuroFlux demonstrates speed-ups of 2.3x to 6.1x on various hardware platforms under stringent GPU memory budgets, and generates models with 10.9x to 29.4x fewer parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to train special kinds of computer vision models called Convolutional Neural Networks (CNNs) on devices like smartphones or smart home devices. This is important because these devices have limited power and memory, making it hard to train big models. NeuroFlux is a new system that helps solve this problem by changing the way we train CNNs. It uses two clever ideas: one reduces how much memory is needed, and the other makes training faster. By breaking down the model into smaller pieces and using special helper networks, NeuroFlux makes it possible to train these models quickly on devices with limited resources. |
Keywords
* Artificial intelligence * Backpropagation * Cnn * Neural network
