Add to ORKGThis thesis is focused on the use of timing speculation to improve the performance and energy efficiency of hardware accelerators. Timing speculation is the use of a circuit using a frequency or a voltage at which its operation is no longer guaranteed. It increases the performance of the circuit (computations per second) but also its energy efficiency (computations per joule). As the correct operation of the circuit is no longer guaranteed, it must be accompanied by an error detection mechanism. This mechanism must have the lowest possible additional cost in terms of resources used, energy and impact on performance. These overheads must indeed be low enough to make the approach worthwhile, but also be as low as possible to maximize the gain...
Low-power consumption has become an important aspect of processors and systems design. Many techniqu...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
Convolutional Neural Networks (CNNs) are extensively used in a wide range of applications, commonly ...
This thesis is focused on the use of timing speculation to improve the performance and energy effici...
Cette thèse porte sur l'utilisation de la spéculation temporelle pour améliorer les performances et ...
In this paper, we propose a technique for improving the efficiency of hardwareaccelerators based on ...
International audienceIn this article, we propose a technique for improving the efficiency of convol...
Timing guardbands act as a barrier protecting conventional processors from circuit-level phenomena l...
The aim of this master thesis is modeling of neural network accelerators with HW support for quantiz...
Microelectronic scaling has entered into the nanoscale era with tremendous capacity and performance ...
Modern microprocessors such as CPU, GPU, and the recent deep learning accelerators exhibit significa...
AI evolution is accelerating and Deep Neural Network (DNN) inference accelerators are at the forefro...
During the last years, Convolutional Neural Networks have been used for different applications thank...
As AI applications become more prevalent and powerful, the performance of deep learning neural netwo...
Timing speculation is a promising approach to increase the processor performance and energy efficien...
Low-power consumption has become an important aspect of processors and systems design. Many techniqu...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
Convolutional Neural Networks (CNNs) are extensively used in a wide range of applications, commonly ...
This thesis is focused on the use of timing speculation to improve the performance and energy effici...
Cette thèse porte sur l'utilisation de la spéculation temporelle pour améliorer les performances et ...
In this paper, we propose a technique for improving the efficiency of hardwareaccelerators based on ...
International audienceIn this article, we propose a technique for improving the efficiency of convol...
Timing guardbands act as a barrier protecting conventional processors from circuit-level phenomena l...
The aim of this master thesis is modeling of neural network accelerators with HW support for quantiz...
Microelectronic scaling has entered into the nanoscale era with tremendous capacity and performance ...
Modern microprocessors such as CPU, GPU, and the recent deep learning accelerators exhibit significa...
AI evolution is accelerating and Deep Neural Network (DNN) inference accelerators are at the forefro...
During the last years, Convolutional Neural Networks have been used for different applications thank...
As AI applications become more prevalent and powerful, the performance of deep learning neural netwo...
Timing speculation is a promising approach to increase the processor performance and energy efficien...
Low-power consumption has become an important aspect of processors and systems design. Many techniqu...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
Convolutional Neural Networks (CNNs) are extensively used in a wide range of applications, commonly ...