Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems

• URL: http://arxiv.org/abs/2107.09101v1
• Date: Mon, 19 Jul 2021 18:43:17 GMT
• Title: Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems
• Authors: Stavros Nousias, Erion-Vasilis Pikoulis, Christos Mavrokefalidis, Aris S. Lalos
• Abstract summary: Automotive Cyber-Physical Systems (ACPS) have attracted a significant amount of interest in the past few decades. One of the most critical operations in these systems is the perception of the environment. Deep learning and, especially, the use of Deep Neural Networks (DNNs) provides impressive results in analyzing and understanding complex and dynamic scenes from visual data.
• Score: 2.4373900721120285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automotive Cyber-Physical Systems (ACPS) have attracted a significant amount of interest in the past few decades, while one of the most critical operations in these systems is the perception of the environment. Deep learning and, especially, the use of Deep Neural Networks (DNNs) provides impressive results in analyzing and understanding complex and dynamic scenes from visual data. The prediction horizons for those perception systems are very short and inference must often be performed in real time, stressing the need of transforming the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques. Our goal in this work is to investigate best practices for appropriately applying novel weight sharing techniques, optimizing the available variables and the training procedures towards the significant acceleration of widely adopted DNNs. Extensive evaluation studies carried out using various state-of-the-art DNN models in object detection and tracking experiments, provide details about the type of errors that manifest after the application of weight sharing techniques, resulting in significant acceleration gains with negligible accuracy losses.

Related papers

• Towards Scalable and Versatile Weight Space Learning [51.78426981947659]
  This paper introduces the SANE approach to weight-space learning. Our method extends the idea of hyper-representations towards sequential processing of subsets of neural network weights.
  arXiv  Detail & Related papers  (2024-06-14T13:12:07Z)
• Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective [64.04617968947697]
  We introduce a novel data-model co-design perspective: to promote superior weight sparsity. Specifically, customized Visual Prompts are mounted to upgrade neural Network sparsification in our proposed VPNs framework.
  arXiv  Detail & Related papers  (2023-12-03T13:50:24Z)
• Precision Machine Learning [5.15188009671301]
  We compare various function approximation methods and study how they scale with increasing parameters and data. We find that neural networks can often outperform classical approximation methods on high-dimensional examples. We develop training tricks which enable us to train neural networks to extremely low loss, close to the limits allowed by numerical precision.
  arXiv  Detail & Related papers  (2022-10-24T17:58:30Z)
• Asymptotic-Preserving Neural Networks for hyperbolic systems with diffusive scaling [0.0]
  We show how Asymptotic-Preserving Neural Networks (APNNs) provide considerably better results with respect to the different scales of the problem when compared with standard DNNs and PINNs.
  arXiv  Detail & Related papers  (2022-10-17T13:30:34Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
  We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
  arXiv  Detail & Related papers  (2021-12-15T20:09:20Z)
• A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks [2.7393821783237184]
  Model Compression and Acceleration (MCA) techniques are used to transform large pre-trained networks into smaller models. We propose a clustering-based approach that is able to increase the number of employed centroids/representatives. This is achieved by imposing a special structure to the employed representatives, which is enabled by the particularities of the problem at hand.
  arXiv  Detail & Related papers  (2021-07-19T18:22:07Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• A Survey on Impact of Transient Faults on BNN Inference Accelerators [0.9667631210393929]
  Big data booming enables us to easily access and analyze the highly large data sets. Deep learning models require significant computation power and extremely high memory accesses. In this study, we demonstrate that the impact of soft errors on a customized deep learning algorithm might cause drastic image misclassification.
  arXiv  Detail & Related papers  (2020-04-10T16:15:55Z)
• The large learning rate phase of deep learning: the catapult mechanism [50.23041928811575]
  We present a class of neural networks with solvable training dynamics. We find good agreement between our model's predictions and training dynamics in realistic deep learning settings. We believe our results shed light on characteristics of models trained at different learning rates.
  arXiv  Detail & Related papers  (2020-03-04T17:52:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.