Add to ORKGDeep learning-based solutions and, in particular, deep neural networks (DNNs) are at the heart of several functionalities in critical-real time embedded systems (CRTES) from vision-based perception (object detection and tracking) systems to trajectory planning. As a result, several DNN instances simultaneously run at any time on the same computing platform. However, while modern GPUs offer a variety of computing elements (e.g. CPUs, GPUs, and specific accelerators) in which those DNN tasks can be executed depending on their computational requirements and temporal constraints, current DNNs are mainly programmed to exploit one of them, namely, regular cores in the GPU. This creates resource imbalance and under-utilization of GPU resources whe...
Deep neural networks (DNNs) are computationally and memory intensive, which makes them difficult t...
The most widely used machine learning frameworks require users to carefully tune their memory usage ...
RISC-V is an open-source instruction set and now has been examined as a universal standard to unify ...
Nowadays, Deep learning-based solutions and, in particular, deep neural networks (DNNs) are getting ...
Deep learning-based solutions and, in particular, deep neural networks (DNNs) are at the heart of se...
Recently, renewed attention to Artificial Intelligence has emerged thanks to algorithms called Deep ...
Presented at DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous ...
Our work seeks to improve and adapt computing systems and machine learning (ML) algorithms to match ...
International audienceDeep neural networks (DNNs) are computationally and memory intensive, which ma...
The invention of deep belief network (DBN) provides a powerful tool for data modeling. The key advan...
On-device DNN processing has been common interests in the field of autonomous driving research. For ...
Deep neural network (DNN) inference is increasingly being executed on mobile and embedded platforms ...
The aim of this project is to conduct a study of deep learning on multi-core processors. The study i...
Design of hardware accelerators for neural network (NN) applications involves walking a tight rope a...
Recent advances in Deep Learning (DL) research have been adopted in a wide variety of applications, ...
Deep neural networks (DNNs) are computationally and memory intensive, which makes them difficult t...
The most widely used machine learning frameworks require users to carefully tune their memory usage ...
RISC-V is an open-source instruction set and now has been examined as a universal standard to unify ...
Nowadays, Deep learning-based solutions and, in particular, deep neural networks (DNNs) are getting ...
Deep learning-based solutions and, in particular, deep neural networks (DNNs) are at the heart of se...
Recently, renewed attention to Artificial Intelligence has emerged thanks to algorithms called Deep ...
Presented at DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous ...
Our work seeks to improve and adapt computing systems and machine learning (ML) algorithms to match ...
International audienceDeep neural networks (DNNs) are computationally and memory intensive, which ma...
The invention of deep belief network (DBN) provides a powerful tool for data modeling. The key advan...
On-device DNN processing has been common interests in the field of autonomous driving research. For ...
Deep neural network (DNN) inference is increasingly being executed on mobile and embedded platforms ...
The aim of this project is to conduct a study of deep learning on multi-core processors. The study i...
Design of hardware accelerators for neural network (NN) applications involves walking a tight rope a...
Recent advances in Deep Learning (DL) research have been adopted in a wide variety of applications, ...
Deep neural networks (DNNs) are computationally and memory intensive, which makes them difficult t...
The most widely used machine learning frameworks require users to carefully tune their memory usage ...
RISC-V is an open-source instruction set and now has been examined as a universal standard to unify ...
