Mixed Precision Training

Structured data

A study conducted in the UK from 2009 to 2010 by leading scientists explored neonatal resuscitation practices in various neonatal units, aiming to assess adherence to international guidelines and identify differences between tertiary and non-tertiary care providers...

One Sentence Abstract

This research presents a methodology for training Cardiology Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. deep neural networks with half-precision floating-point numbers, which significantly1 reduces memory requirements and increases computational speed, while maintaining accuracy and not requiring modified hyper-parameters, by implementing three techniques to prevent loss of critical information.

Simplified Abstract

Researchers have developed a new method to train large, complex neural networks without needing as much memory or computing power. This is important because making a network bigger usually improves its accuracy, but it also requires more resources. The new technique, called "half-precision," uses less space for data storage while still maintaining high accuracy.

To ensure that the network stays accurate, the researchers suggest three strategies. First, they recommend keeping a full-precision copy of certain parts of the network. Second, they propose adjusting the loss scale to preserve small details. Lastly, they use a special type of math that combines both full and half-precision calculations.

The researchers show that this new method works well with various tasks and large models that have more than 100 million parameters. By using half-precision, networks can be trained more quickly and with less space needed, making it easier and faster for scientists to build smarter artificial intelligence systems.

Subheader 1

Subheader 2

Subheader 3

Study Fields

Main Field: Efficient Training of Deep Neural Networks

Subfields:

Utilizing Half-Precision Floating Point Numbers
Memory Requirements Reduction
Speeding up Arithmetic
Preventing Loss of Critical Information
Single-Precision Copy of Weights
Gradients Accumulation in Single-Precision
Half-Precision Rounding
Loss-Scaling
Single-Precision Outputs and Half-Precision Conversion
Model Architectures and Large Datasets

Study Objectives

Subheader 1

Subheader 2

To explore the possibility of training deep neural networks using half-precision floating point numbers without losing model accuracy or modifying hyper-parameters.
To reduce memory requirements by nearly half and speed up arithmetic on recent GPUs.
To propose and test three techniques for preventing the loss of critical information when using half-precision format:
- Maintaining a single-precision copy of weights that accumulates gradients after each optimizer step.
- Loss-scaling to preserve gradient values with small magnitudes.
- Half-precision arithmetic that accumulates into single-precision outputs, converted to half-precision before storage.
To demonstrate the effectiveness of the proposed methodology across a wide range of tasks, large-scale model architectures (exceeding 100 million parameters), and large datasets.

Conclusions

The authors present a methodology for training deep neural networks using half-precision floating point numbers, which significantly reduces memory requirements and increases arithmetic speed.
They store weights, activations, and gradients in IEEE half-precision format, but recognize that this narrower range may lead to loss of critical information.
To prevent such loss, they propose three techniques: a) maintaining a single-precision copy of weights, b) loss-scaling, and c) using half-precision arithmetic that accumulates into single-precision outputs.
The methodology demonstrates effectiveness across various tasks, large-scale model architectures (over 100 million parameters), and big datasets.
By nearly halving memory requirements and speeding up arithmetic, this methodology could improve the efficiency of training deep neural networks.

References

K. He, X. Zhang, S. Ren, J. Sun•

K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016a.

Mixed Precision Training

One Sentence Abstract

Simplified Abstract

Subheader 1

Subheader 2

Subheader 3

Study Fields

Study Objectives

Subheader 1

Subheader 2

Conclusions

References

References

Unlock full article access by joining Solve