Add to ORKGDeep Convolutional Networks (ConvNets) are currently superior in benchmark performance, but the associated demands on computation and data transfer prohibit straightforward mapping on energy constrained wearable platforms. The computational burden can be overcome by dedicated hardware accelerators, but it is the sheer amount of data transfer, and level of utilization that determines the energy-efficiency of these implementations. This paper presents the Neuro Vector Engine (NVE) a SIMD accelerator for ConvNets for visual object classification, targeting portable and wearable devices. Our accelerator is very flexible due to the usage of VLIW ISA, at the cost of instruction fetch overhead. We show that this overhead is insignificant when the ...
The main objective of an Artificial Vision Algorithm is to design a mapping function that takes an i...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
Today advanced computer vision (CV) systems of ever increasing complexity are being deployed in a gr...
Deep Convolutional Networks (ConvNets) are currently superior in benchmark performance, but the asso...
Deep Convolutional Networks (ConvNets) are currently superior in benchmark performance, but the asso...
© 2017 IEEE. ConvNets, or Convolutional Neural Networks (CNN), are state-of-the-art classification a...
© 2016 IEEE. Recently convolutional neural networks (ConvNets) have come up as state-of-the-art clas...
Computer vision (CV) based on Convolutional Neural Networks (CNN) is a rapidly developing field than...
The presented paper proposes a novel, hybrid neuromorphic computational architecture for visual data...
Convolutional neural networks (ConvNets) are hierarchical models of the mammalian visual cortex. The...
This paper presents a compiler flow to map Deep Convolutional Networks (ConvNets) to a highly specia...
The advantages of Convolutional Neural Networks (CNNs) with respect to traditional methods for visua...
Convolutional neural networks have been widely employed for image recognition applications because o...
The main objective of an Artificial Vision Algorithm is to design a mapping function that takes an i...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
Today advanced computer vision (CV) systems of ever increasing complexity are being deployed in a gr...
Deep Convolutional Networks (ConvNets) are currently superior in benchmark performance, but the asso...
Deep Convolutional Networks (ConvNets) are currently superior in benchmark performance, but the asso...
© 2017 IEEE. ConvNets, or Convolutional Neural Networks (CNN), are state-of-the-art classification a...
© 2016 IEEE. Recently convolutional neural networks (ConvNets) have come up as state-of-the-art clas...
Computer vision (CV) based on Convolutional Neural Networks (CNN) is a rapidly developing field than...
The presented paper proposes a novel, hybrid neuromorphic computational architecture for visual data...
Convolutional neural networks (ConvNets) are hierarchical models of the mammalian visual cortex. The...
This paper presents a compiler flow to map Deep Convolutional Networks (ConvNets) to a highly specia...
The advantages of Convolutional Neural Networks (CNNs) with respect to traditional methods for visua...
Convolutional neural networks have been widely employed for image recognition applications because o...
The main objective of an Artificial Vision Algorithm is to design a mapping function that takes an i...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
Today advanced computer vision (CV) systems of ever increasing complexity are being deployed in a gr...