Add to ORKGThis paper presents a state-of-the-art overview on how to architect, design, and optimize Deep Neural Networks (DNNs) such that performance is improved and accuracy is preserved. The paper covers a set of optimizations that span the entire Machine Learning processing pipeline. We introduce two types of optimizations. The first alters the DNN model and requires NN re-training, while the second does not. We focus on GPU optimizations, but we believe the presented techniques can be used with other AI inference platforms. To demonstrate the DNN model optimizations, we improve one of the most advanced deep network architectures for optical flow, RAFT arXiv:2003.12039, on a popular edge AI inference platform (Nvidia Jetson AGX Xavier).Comment: 8 ...
With the growing demand for vision applications and deployment across edge devices, the development ...
Deep neural networks (DNNs) have succeeded in many different perception tasks, e.g., computer vision...
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem do...
Rapid progress in deep learning is leading to a diverse set of quickly changing models, with a drama...
In recent years, the accuracy of Deep Neural Networks (DNNs) has improved significantly because of t...
Convolutional neural networks (CNNs) have produced unprecedented accuracy for many computer vision p...
The success of deep neural networks (DNNs) is attributable to three factors: increased compute capac...
A number of competing concerns slow adoption of deep learning for computer vision on“edge” devices. ...
Training deep neural networks consumes increasing computational resource shares in many compute cent...
This paper describes a methodology to select the optimum combination of deep neuralnetwork and softw...
Deep Neural Networks (DNNs) are extremely computationally demanding, which presents a large barrier ...
Deep Neural Networks (DNNs) have achieved great success in a massive number of artificial intelligen...
Deep neural networks (DNNs) are widely used by both academic and industry researchers to solve many ...
Thesis (Ph.D.)--University of Washington, 2021Efficient hardware, increased computational power, an...
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is alre...
With the growing demand for vision applications and deployment across edge devices, the development ...
Deep neural networks (DNNs) have succeeded in many different perception tasks, e.g., computer vision...
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem do...
Rapid progress in deep learning is leading to a diverse set of quickly changing models, with a drama...
In recent years, the accuracy of Deep Neural Networks (DNNs) has improved significantly because of t...
Convolutional neural networks (CNNs) have produced unprecedented accuracy for many computer vision p...
The success of deep neural networks (DNNs) is attributable to three factors: increased compute capac...
A number of competing concerns slow adoption of deep learning for computer vision on“edge” devices. ...
Training deep neural networks consumes increasing computational resource shares in many compute cent...
This paper describes a methodology to select the optimum combination of deep neuralnetwork and softw...
Deep Neural Networks (DNNs) are extremely computationally demanding, which presents a large barrier ...
Deep Neural Networks (DNNs) have achieved great success in a massive number of artificial intelligen...
Deep neural networks (DNNs) are widely used by both academic and industry researchers to solve many ...
Thesis (Ph.D.)--University of Washington, 2021Efficient hardware, increased computational power, an...
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is alre...
With the growing demand for vision applications and deployment across edge devices, the development ...
Deep neural networks (DNNs) have succeeded in many different perception tasks, e.g., computer vision...
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem do...