Nvidia corporation (20240127067). SHARPNESS-AWARE MINIMIZATION FOR ROBUSTNESS IN SPARSE NEURAL NETWORKS simplified abstract

SHARPNESS-AWARE MINIMIZATION FOR ROBUSTNESS IN SPARSE NEURAL NETWORKS

Organization Name

nvidia corporation

Inventor(s)

Annamarie Bair of Pittsburgh PA (US)

Hongxu Yin of San Jose CA (US)

Pavlo Molchanov of Mountain View CA (US)

Maying Shen of Fremont CA (US)

Jose Manuel Alvarez Lopez of Mountain View CA (US)

SHARPNESS-AWARE MINIMIZATION FOR ROBUSTNESS IN SPARSE NEURAL NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240127067 titled 'SHARPNESS-AWARE MINIMIZATION FOR ROBUSTNESS IN SPARSE NEURAL NETWORKS

Simplified Explanation

The patent application discusses systems and methods for enhancing the natural robustness of sparse neural networks through the use of sharpness-aware minimization (SAM) optimization during training. By pruning a dense neural network, the resulting sparse network can achieve improved inference speed, reduced memory footprint, and lower energy consumption while maintaining accuracy. SAM optimization helps the network perform better on out-of-distribution images compared to conventional stochastic gradient descent optimization by finding a flat minimum that balances low loss values with a region of low loss.

• Pruning dense neural networks can enhance inference speed, reduce memory footprint, and lower energy consumption.
• Sharpness-aware minimization (SAM) optimization during training improves performance on out-of-distribution images compared to stochastic gradient descent optimization.
• SAM optimization aims to find a flat minimum that balances low loss values with a region of low loss.

Potential Applications

The technology can be applied in autonomous vehicles for tasks like object detection and classification, where robustness to various environmental conditions is crucial.

Problems Solved

The innovation addresses the challenge of maintaining accuracy while reducing the computational and memory requirements of neural networks, particularly in real-world scenarios with sparse networks.

Benefits

The benefits of this technology include improved inference speed, reduced energy consumption, and enhanced performance on out-of-distribution images, leading to more robust neural networks.

Potential Commercial Applications

Potential commercial applications include autonomous vehicles, robotics, surveillance systems, and any other systems where efficient and robust neural networks are required.

Possible Prior Art

Prior art may include research on neural network pruning techniques, optimization algorithms for training neural networks, and methods for improving network robustness to out-of-distribution data.

How does SAM optimization specifically improve performance on out-of-distribution images compared to traditional optimization methods?

SAM optimization aims to find a flat minimum that balances low loss values with a region of low loss, which helps the network generalize better to unseen data. However, the exact mechanism through which SAM achieves this improvement could be further explored.

What are the potential limitations or drawbacks of pruning dense neural networks to create sparse networks?

While pruning can offer benefits such as improved speed and reduced memory usage, it may also lead to loss of information or accuracy if not done carefully. Understanding the trade-offs involved in pruning and its impact on network performance is essential for practical applications.

Original Abstract Submitted

systems and methods are disclosed for improving natural robustness of sparse neural networks. pruning a dense neural network may improve inference speed and reduces the memory footprint and energy consumption of the resulting sparse neural network while maintaining a desired level of accuracy. in real-world scenarios in which sparse neural networks deployed in autonomous vehicles perform tasks such as object detection and classification for acquired inputs (images), the neural networks need to be robust to new environments, weather conditions, camera effects, etc. applying sharpness-aware minimization (sam) optimization during training of the sparse neural network improves performance for out of distribution (ood) images compared with using conventional stochastic gradient descent (sgd) optimization. sam optimizes a neural network to find a flat minimum: a region that both has a small loss value, but that also lies within a region of low loss.